Continuously Engineering Application Performance

The success of an application today hinges on customer experience. To a large extent, it’s the sum of two components, one being the applicability of the software product features to the target audience and the second, the experience of the customer while using the application. In October 2021, a six-hour outage of the Facebook family of apps cost the company nearly $100 million in revenue. Instances like these underline the need to focus on application performance for a good customer experience. We are witnessing an era of zero patience, making application speed, availability, reliability, and stability more paramount to product release success. 

Modern application development cycles are agile, or DevOps led, effectively addressing application functionality through MVP and subsequent releases. However, the showstopper in many cases is application underperformance. This is an outcome of the inability of an organization to spend enough time analyzing release performance in real-life scenarios. Even in agile teams, performance testing happens one sprint behind other forms of testing. With an increase in the number of product releases, the number of times application performance checks can be done, and the window available to do full-fledged performance testing is reducing.

How do you engineer for performance?

Introducing performance checks & testing early in the application development lifecycle helps to detect issues, identify potential performance bottlenecks early on and take corrective measures before they have a chance to compound over subsequent application releases. This also brings to the fore predictive performance engineering – the ability to foresee and provide timely advice on vulnerable areas. By focusing on areas outlined in the subsequent sections, organizations can move towards continuously engineering applications for superior performance rather than a testing application for superior performance.

Adopt a performance mindset focused on risk and impact

Adopting a performance mindset the moment a release is planned can help anticipate many common performance issues. The risks applicable to these issues can be classified based on various parameters like scalability, capacity, efficiency, resilience, etc. The next step is to ascertain the impact those risks can have on the application performance, which can further be used to stack rank the performance gaps and take remedial measures.

An equally important task is the choice of tools/platforms adopted in line with the mindset. For, e.g., evaluating automation capability for high scale load testing, bringing together insights on the client as well as server-side performance & troubleshooting, or carrying out performance testing with real as well as virtual devices, all the while mapping such tools against risk impact metrics.

Design with performance metrics in mind

Studies indicate that many performance issues remain unnoticed during the early stages of application development. With each passing release, they mount up before the application finally breaks down when it encounters a peak load. When that happens, there arises a mandate to revisit all previous releases from a performance point of view, which is a cumbersome task. Addressing this issue calls for a close look at behaviors that impact performance and building them into the design process.

·         Analyzing variations or deviations in past metrics from component tests,

·         Extending static code analysis to understand performance impacts/flaws, and

·      Dynamic code profiling to understand how the code performs during execution, thereby exposing runtime vulnerabilities.

Distribute performance tests across multiple stages

Nothing could be more error-prone than scheduling performance checks towards the end of the development lifecycle. When testing each build, it makes a lot more sense to incorporate performance-related checks as well. At the unit level, you can have a service component test for analyzing at an individual service level and a product test focusing on the entire release delivered by the team. Break testing individual components continuously through fast, repeatable performance tests will help to understand their tolerances and dependencies on other modules.

For either of the tests mentioned above, mocks need to be created early to ensure that interfaces to downstream services are taken care of, without dependency on those services to be up and running. This should be followed by assessing integration performance risk whereby code developed by multiple DevOps teams is brought together. Performance data across each build can be fed back to take corrective actions along the way. Continuously repeating runs of smaller tests and providing real-time feedback to the developers help them understand the code development much better and quickly make improvements to the code.

Evaluate application performance at each stage of the CI/CD pipeline

Automating and integrating performance testing into the CI/CD process involves unit performance testing at the code & build stages, integration performance testing when individual software units are integrated, system-level performance testing and load testing, and real user monitoring when the application moves into a production environment. Prior to going live, it would be good to test the performance of the complete release to get an end-to-end view.

Organizations that automate and integrate performance tests into the CI/CD process are a common practice that runs short tests as part of the CI cycle unattended. What is needed is the ability to monitor the test closely as it runs and look for anomalies or signs of failure that point to a corrective action to be taken on the environment or on the scripts as well as application code. Metrics from these tests can be compared to performance benchmarks created as part of the design stage. The extent of deviations from benchmarks can point to code-level design factors causing performance degradation.

Assess performance in a production environment

Continuous performance monitoring happens after the application goes live. The need at this stage is to monitor application performance through dashboards, alerts, etc., and compare those with past records and benchmarks. The analysis can then decode performance reports across stages to foresee risks and provide amplified feedback into the application design stage.

Another important activity that can be undertaken at this stage is to monitor end-user activity and sentiment for performance. The learnings can further be incorporated into the feedback loop driving changes to subsequent application releases.

Continuously engineer application performance with Trigent

Continuously engineering application performance plays a critical role in improving the apps’ scalability, reliability, and robustness before they are released into the market. With years of expertise in quality engineering, Trigent can help optimize your application capacity, address availability irrespective of business spikes and dips, and ensure first-time-right product launches and superior customer satisfaction and acceptance.

Does your QA meet all your application needs? Let’s connect and discuss

QE strategy to mitigate inherent risks involved in application migration to the cloud

Cloud migration strategies, be it lift & shift, rearchitect or rebuild, are fraught with inherent risks which need to be eliminated with the right QE approach

The adoption of cloud environments has been expanding for several years and is presently in an accelerated mode. A multi-cloud strategy is the defacto approach adopted by multiple organizations, as per Flexera 2022 State of the Cloud Report. The move toward cloud-native application architectures, exponential scaling needs of applications, and increased frequency and speed of product release launches have contributed to increased cloud adoption.

The success of migrating the application landscape to the cloud hinges on the ability to perform end-to-end quality assurance initiatives specific to the cloud. 

Underestimation of application performance

Availability, scalability, reliability, and high response rates are critical expectations from an application in a cloud environment. Application performance issues can come to light on account of incorrect sizing of servers or network latency issues that might not have surfaced when the application is tested in isolation. It can also be an outcome of an incorrect understanding of probable workloads that can be managed by an application while in a cloud environment. 

The right performance engineering strategy involves designing for performance in mind and fulfilling performance validations, including load testing. This ensures that the application under test remains stable in normal and peak conditions and defines and sets up application monitoring toolsets and parameters. There needs to be an understanding of workloads with the potential to be moved to the cloud and ones that need to remain on-premise. Incompatible application architectures need to be identified. Load testing should be carried out in parallel to record SLA response times across various loads for those moved to the cloud. 

Security and compliance

With the increased adoption of data privacy norms like GDPR and CCPA, there is a renewed focus on ensuring the safety of data migrated from application to cloud. Incidents like the one with Marriott hotel, where half a million sensitive customer information like credit cards and identity were compromised, have brought the need to test the security of data loaded onto cloud environments. 

A must-have element of a sound QA strategy is to ensure that both applications and data are secure and can withstand malicious attacks. With cybersecurity attacks increasing both in quantity and innovative tactics, there is a strong need for the implementation of security policies and testing techniques, including but not limited to vulnerability scanning, penetration testing, and threat and risk assessment. These are aimed at the following.

  • Identifying security gaps and weaknesses in the system
  • DDoS attack prevention
  • Provide actionable insights on ways to eliminate potential vulnerabilities

Accuracy of Data migration 

Assuring the quality of data that is being migrated to the cloud remains the top challenge, without which the convenience and performance expectation from cloud adoption falls flat. It calls for assessing quality before migrating, monitoring during migration, and verifying the integrity and quality post-migration. This is fraught with multiple challenges like migrating from old data models, duplicate record management, and resolving data ownership, to name a few. 

White-box migration testing forms a key component of a robust data migration testing initiative. It starts off by logically verifying a migration script to guarantee it’s complete and accurate. This is followed by ensuring database compliance with required preconditions, e.g., detailed script description, source, and receiver structure, and data migration mapping. Furthermore, the QA team analyzes and assures the structure of the database, data storage formats, migration requirements, the formats of fields, etc. More recently, predictive data quality measures have also been adopted to get a centralized view and better control over data quality. 

Application Interoperability

Not all apps that need to migrate to the cloud may be compatible with the cloud environment. Some applications show better performance in a private or hybrid cloud than in a public cloud. Some others require minor tweaking, while others may require extensive reengineering or recoding. Not identifying cross-application dependencies before planning the migration waves can lead to failure. Equally important is the need to integrate with third-party tools for seamless communication across applications without glitches. 

A robust QA strategy needs to identify applications that are part of the network, their functionalities, and dependencies among applications, along with each app’s SLA since dependencies between systems and applications can make integration testing potentially challenging. Integration testing for cloud-based applications brings to the fore the need to consider the following: 

  • Resources for the validation of integration testing 
  • Assuring cloud migration by using third-party tools
  • Discovering glitches in coordination within the cloud
  • Application configuration in the cloud environment
  • Seamless integration across multiple surround applications

Ensure successful cloud migration with Trigent’s QE services

Application migration to the cloud can be a painful process without a robust QE strategy. With aspects such as data quality, security, app performance, and seamless connection with a host of surrounding applications being paramount in a cloud environment, the need for testing has become more critical than ever. 

Trigent’s cloud-first strategy enables organizations to leverage a customized, risk-mitigated cloud strategy and deployment model most suitable for the business. Our proven approach, frameworks, architectures, and partner ecosystem have helped businesses realize the potential of the cloud.

We provide a secure, seamless journey from in-house IT to a modern enterprise environment powered by Cloud. Our team of experts has enabled cloud transformation at scale and speed for small, medium, and large organizations across different industries. The transformation helps customers leverage the best architecture, application performance, infrastructure, and security without disrupting business continuity. 

Ensure a seamless cloud migration for your application. Contact us now!

Intelligent Test Automation in a DevOps World

The importance of intelligent test automation

Digital transformation has disrupted time to market like never before. Reducing the cycle time for releasing multiple application versions through the adoption of Agile and DevOps principles has become the prime factor for providing a competitive edge. However, assuring quality across application releases is now proving to be an elusive goal in the absence of the right amount of test automation. Hence it is no surprise that according to the Data Bridge Market research, the automation testing market will reach an estimated value of USD 19.9 billion by 2028 and grow at a CAGR of 14.89% in the forecast period of 2021 to 2028.

Test automation is a challenge, not only because an organization’s capabilities have traditionally been focused on manual testing techniques but also because it’s viewed to be a complex siloed activity. Automation engineers are expected to cohesively bind the vision of the business team, functional flows that the testers use, along their own core component of automation principles and practices. Production continuum can only become a reality when there is a disruption to automation as a siloed activity, ably supported by maximum collaboration and convergence of skillsets. Even then, without the adoption of the right test automation techniques, it becomes near impossible to realize the complete value.

Outlined below are steps towards making test automation initiatives more effective and results-oriented.

Comprehensive coverage of test scenarios

Test automation, to a large extent, focuses on the lower part of the test pyramid addressing viz – unit testing and component testing but neglecting the most crucial aspect of testing business-related areas. The key to assuring application quality is to identify those scenarios that are business relevant and automate them for maximum test coverage. The need of the hour is to adopt tools and platforms that cover the entire test pyramid and not restrict it to any level.

Read more: The right testing strategies for AI/ML applications

A test design-led automation approach can help in ensuring maximum coverage of test scenarios. However, given that this is a complex area, aggravated by the application complexity itself, what tools can help with is to handle the sequence of test scenarios, expressing the business rules and associating data-driven decision tables attached to the workflow, thereby providing complete coverage of all high-risk business cases. By adopting this sequence, complexity can be better managed, modifications can be applied much faster, and tests can be structured to be more automation friendly. 

This approach helps to analyze functional parameters of the test in a better way and helps to define what needs to be tested with sharp focus, i.e., enable a sharper prioritization of the test area. It aggregates various steps involved in test flow along with the conditions each step can have and prioritizes the generation of steps along with risk association.

Ensure 80% test coverage with comprehensive automation testing frameworks. Let’s talk

Sharp focus on test design

The adoption of Test Driven Development (TDD) and Behavior Driven Development (BDD) techniques aims to accelerate the design phase in Agile engagements. However, these techniques come at the cost of incomplete test coverage and test suite maintenance-related issues. Test design automation aims to overcome these challenges by concentrating on areas like requirements engineering, automated test case generation, migration, and optimization. Automation focus at the test design stage contributes to tremendous value-add downstream by removing the substantial load from scripting test cases and generating them. 

Adoption of the right toolsets accelerates the inclusion of test design automation during the earlier stages of the development process, making it key to Agile engagements. Most test design automation tools adopt visual-based testing. They make use of graphical workflows that can be understood by all project stakeholders – testers, business stakeholders, technical experts, etc. Such workflows can be synchronized with any requirements management toolsets and collaboratively improved with inputs from all stakeholders. User stories and acceptance criteria are contextualized so that everyone can see the functional dependency between the previous user stories and the ones that were developed during the current sprint.

Collaboration is key

Collaboration is the pillar of Agile development processes. By bringing collaboration into test design, risk-based coverage of test cases can be effectively addressed, along with the generation of automated scripts on a faster note. Automation techniques steeped in collaboration provide the ability to organize tests by business flows, keywords, impact and ensure depth of test coverage by leveraging the right test data. 

By integrating test automation tools into Agile testing cycles, a collaborative test design can be delivered with ease. With such tools, any changes to user stories can be well reflected; users can comment on the flows or data, identify and flag risks much earlier. These tools also enable the integration of test cases into test management tools of choice like Jira and generate automation scripts that can work under different automation tools like selenium.

Making legacy work

Most organizations suffer from a huge backlog of legacy cases – there is a repository of manual test cases that are critical for business. Organizations need them to be a part of the agile stream. For this to happen, automation is mandatory. Manual test cases of legacy applications are very rich in application functionality and make good sense to get retrofitted into test automation platforms.

New age test design automation frameworks and platforms can address legacy tests that are already documented, parse them, and incorporate them as a part of the automation test suite. Many of these tools leverage AI to retro engineer manual test cases into the software platform – graphical workflow, test data, and test cases themselves can be added to the tool. 

You may also like: Uncovering nuances in data-led QA for AI/ML applications

A closer look at the current test automation landscape outlines a shift from the siloed model that existed earlier. Clearly visible is the move towards automation skillsets, coding practices, and tools-related expertise. Automation tools are also seen moving up the maturity curve to optimize the effort of test automation engineers, at the same time enabling functional testers with minimal exposure to automation stacks to contribute significantly to automation effort. All in all, such shifts are accelerating the move towards providing organizations the ability to do more automation with existing resources.

Trigent’s partnership with Smartesting allows us to leverage test design automation by Integrating these tools in your Agile testing cycles, thus being able to quickly deliver collaborative test design, risk-based coverage of test cases, and faster generation of automated scripts. We help you organize tests by business flows, keywords, risks, depth of coverage, leveraging the right test data, as well as generate and integrate test cases into test management tools of your choice (JIRA, Zephy, Test Rail, etc.).

Our services will enable you to take on your documented legacy tests, parse them and bring them into such tools very quickly. Further, we help you generate test automation scripts that can work under different automation tools like Selenium & Cypress. Our services are delivered in an As-A-Service Model, or you can leverage our support to implement the tools and the training of your teams to achieve their goals.

Ensure seamless functionality and performance of your application with intelligent test automation. Call us now!

DevOps Success: 7 Essentials You Need to Know

High-performing IT teams are always looking for ways to adopt and use industry best practices and solutions. This enables them to overcome obstacles and achieve consistent and reliable commercial outcomes. A DevOps strategy enables the delivery of software products and services to the market in a more reliable and timely manner. The capacity of the team to have the correct combination of human judgment, culture, procedure, tools, and automation is critical to DevOps success.

Is DevOps the Best Approach for You?

DevOps is a solid framework that aids businesses in getting the most out of their digital efforts. It fosters a productive workplace by enhancing cooperation and value generation across all teams, including development, testing, and operations.

DevOps-savvy companies can launch software solutions more quickly into production, with shorter lead times and reduced failure rates. They have higher levels of responsiveness, are more resilient to production difficulties, and restore failed services more quickly.

However, just because every other IT manager is boasting about their DevOps success stories doesn’t mean you should jump in and try your hand at it. By planning ahead for your DevOps journey, you can avoid the traps that are sure to arise.

Here are seven essentials to keep in mind when you plan your DevOps journey.

1. DevOps necessitates a shift in work culture—manage it actively.

The most important feature of DevOps is the seamless integration of various IT teams to enable efficient execution. It results in a software delivery pipeline known as Continuous Integration-Continuous Delivery (CI/CD). Across development, testing, and operations, you must abandon the traditional silo approach and adopt a collaborative and transparent paradigm. Change is difficult and often met with opposition. It is tough for people to change their working habits overnight. You play an important role in addressing such issues in order to achieve cultural transformation. Be patient, persistent, and use continuous communication to build the necessary change in the management process.

2. DevOps isn’t a fix for capability limitations— it’s a way to improve customer experiences

DevOps isn’t a panacea for all of the problems plaguing your existing software delivery. Mismatches between what upper management expects and what is actually possible must be dealt with individually. DevOps will give you a return on your investment over time. Stakeholder expectations about what it takes to deploy DevOps in their organization should be managed by IT leaders.

Obtain top-level management buy-in and agreement on the DevOps strategy, approach, and plan. Define DevOps KPIs that are both attainable and measurable, and make sure that all stakeholders are aware of them.

3. Keep an eye out for going off-track during the Continuous Deployment Run

Only until you can forecast, track, and measure the end-customer advantages of each code deployment in production can you fully implement DevOps’ continuous deployment approach. In each deployment, focus on the features that are important to the business, their importance, plans, development, testing, and release.

At every stage of DevOps, developers, testers, and operations should all contribute to quality engineering principles. This ensures that continuous deployments are stable and reliable.

4. Restructure your testing team and redefine your quality assurance processes

To match with DevOps practices and culture, you must reimagine your testing life cycle process. To adapt and incorporate QA methods into every phase of DevOps, your testing staff needs to be rebuilt and retrained into a quality assurance regimen. Efforts must be oriented toward preventing or catching bugs in the early stages of development, as well as assisting in making every release of code into production reliable, robust, and fit for the company.

DevOps testing teams must evolve from a reactive, bug-hunting team to a proactive, customer-focused, and multi-skilled workforce capable of assisting development and operations.

5. Incorporate security practices earlier in the software development life cycle (SDLC)

Security is typically considered near the end of the IT value chain. This is primarily due to the lack of security knowledge among most development and testing teams. Information security’s confidentiality, integrity, and availability must be ingrained from the start of your SDLC to ensure that the code in production is secure against penetration, vulnerabilities, and threats.

Adopt and use methods and technologies to help your system become more resilient and self-healing. Integrating DevSecOps into DevOps cycles will allow you to combine security-focused mindsets, cultures, processes, tools, and methodologies across your software development life cycle.

6. Only use tools and automation when absolutely necessary

It’s not about automating everything in your software development life cycle with DevOps. DevOps emphasizes automation and the use of tools to improve agility, productivity, and quality. However, in the hurry to automate, one should not overlook the value and significance of the human judgment. From business research to production monitoring, the team draws vital insights and collective intelligence through constant and seamless collaborative efforts that can’t be substituted by any tool or automation.

Managers, developers, testers, security experts, operations, and support teams must collaborate to choose which technologies to utilize and which automation areas to automate. Automate tasks like code walkthroughs, unit testing, integration testing, build verification, regression testing, environment builds, and code deployments that are repetitive.

7. DevOps is still maturing, and there is no standard way to implement it

DevOps is continuously changing, and there is no one-size-fits-all approach or strategy for implementing it. DevOps implementations may be defined, interpreted, and conceptualized differently by different teams within the same organization. This could cause misunderstanding in your organization regarding all of your DevOps transformation efforts. For your company’s demands, you’ll need to develop a consistent method and plan. It’s preferable if you make sure all relevant voices are heard and ideas are distilled in order to produce a consistent plan and approach for your company. Before implementing DevOps methods across the board, conduct research, experiment, and run pilot projects.

(Originally published in Stickyminds)

5 Must-Haves for QA to Fit Perfectly into DevOps

DevOps is the ideal practice for software development businesses that want to code, build, test, and release software continuously. It’s popular because it stimulates cross-skilling and self-improvement by creating a fast-paced, results-oriented, collaborative workplace. QA in DevOps fosters agility, resulting in speedier operational support and fixes that meet stakeholder expectations. Most significantly, it ensures the timely delivery of high-quality software.

Quality Assurance and Testing (QAT) is a critical component of a successful DevOps strategy. QAT is a vital enabler that connects development and operations in a collaborative thread to assure the delivery of high-quality software and applications.

DevOps QA Testing – Integrating QA in DevOps

Five essentials play a crucial role in achieving flawless sync and seamlessly integrating QA into the DevOps process.

1. Concentrate on the Tenets of Testing

Testing is at the heart of QA; hence the greatest and most experienced testers with hands-on expertise must be on board. Some points to consider: the team must maintain a strong focus on risk, include critical testing thinking into the functional and non-functional parts of the application, and not lose sight of the agile testing quadrant’s needs. Working closely with the user community, pay particular attention to end-user experience tests.

2. Have relevant technical skills and a working knowledge of tools and frameworks

While studying and experimenting with the application is required, a thorough understanding of the unique development environment is also required. This guarantees that testers contribute value to the design stage talks and advise the development team on possibilities and restrictions. They must also be familiar with the operating environment and, most significantly, the application’s performance in the actual world.

The team’s skill set should also include a strong understanding of automation and the technologies required, as this adds rigor to the DevOps process and is necessary to keep it going. The QA team’s knowledge must be focused on tools, frameworks, and technologies. What should their automation strategy be? Do they advocate or utilize licensed or open-source software?

Are the tools for development, testing, deployment, and monitoring identified for the various software development life cycle stages? To avoid delays and derailing the process at any stage of development, it is critical to have comprehensive clarity on the use of tools. Teams with the competence should be able to experiment with technologies like artificial intelligence or machine learning to give the process a boost.

3. Agile Testing Methodologies

DevOps is synchronized agility that incorporates development, quality assurance, and operations. It’s a refined version of the agile testing technique. Agile/DevOps techniques now dominate software development and testing. Can the QA team ensure that in an Agile/DevOps environment, the proper coverage, aligned to the relevant risks, enhances velocity? Is the individual or group familiar with working in a fast-paced environment? Do they have the mechanisms to ensure continuous integration, development, testing, and deployment in cross-functional, distributed teams?

4. Industry experience that is relevant

Relevant industry knowledge ensures that the testers are aware of user experiences and their potential influence on business and can predict potential bottlenecks. Industry expertise improves efficiency and helps testers select testing that has the most impact on the company.

5. The Role of Culture

In DevOps, the QA team’s culture is a crucial factor. The DevOps methodology necessitates that QA team members be alert, quick to adapt, ethical, and work well with the development and operations teams. They serve as a link between the development and operations teams, and they are responsible for maintaining balance and ensuring that the process runs smoothly.

In a DevOps process, synchronizing the three pillars (development, quality assurance, and operations) is crucial for software products to fulfill expectations and deliver commercial value. The QA team serves as a link in this process, ensuring that software products transfer seamlessly from development to deployment. What factors do you believe QA can improve to integrate more quickly and influence the DevOps process?

(Originally published in Stickyminds)

QA in Cloud Environment – Key Aspects that Mandate a Shift in the QA Approach

Cloud computing is now the foundation for digital transformation. Starting as a technology disruptor a few years back, it has become the de facto approach for technology transformation initiatives. However, many organizations still struggle to optimize cloud adoption. Reasons abound – ranging from lack of a cohesive cloud strategy to mindset challenges in adopting cloud platforms. Irrespective of the reason, assuring the quality of applications in cloud environments remains a prominent cause for concern.

Studies indicate a wastage of $17.6 billion in cloud spend in 2020 due to multiple factors like idle resources, overprovisioning, and orphaned volumes and snapshots (Source parkmycloud.com). Further, some studies have pegged the cost of software bugs to be 1.1 trillion dollars. Assuring the quality of any application hosted on the cloud not only addresses its functional validation but also its performance-related aspects like load testing, stress testing, capacity planning, etc invariably addressing both the issues described above, thereby exponentially reducing the quantum of loss incurred on account of poor quality.

The complication for QA in cloud-based application arises due to many deployment models ranging from private cloud, public cloud to hybrid cloud, and application service models ranging from IaaS, PaaS, to SaaS. While looking at deployment models, testers will need to address infrastructure aspects and application quality. At the same time, while paying attention to service models, QA will need to focus on the team’s responsibilities regarding what they own, manage, and delegate.

Key aspects that mandate a shift in the QA approach in cloud-based environments are –

Application architecture

Earlier and to some extent even now, when it comes to legacy applications, QA primarily deals with a monolithic architecture. The onus was on understanding the functionality of the application and each component that made up the application, i.e., QA was not just black-box testing. The emergence of the cloud brought with it a shift to microservices architecture, which completely changed testing rules.

Multiple scrum teams work on various application components or modules deployed in containers and connected through APIs in a microservices-based application. The containers have a communication mechanism based on contracts. QA methodology for cloud-based applications is very different from that adopted for monolith applications and therefore requires detailed understanding.

Security, compliance, and privacy

In typical multi-cloud and hybrid cloud environments, the application is hosted in a 3rd party environment or multiple 3rd party environments. Such environments can also be geographically distributed, with data centers housing the information residing in numerous countries. Regulations that restrict data movement outside countries, service models that call for multi-region deployment, and corresponding data storage and access without impinging on regulatory norms need to be understood by QA personnel.QA practitioners also need to be aware of the data privacy rules existing across regions.

The rise of the cloud has given way to a wide range of cybersecurity issues – techniques for intercepting data and hacking sensitive data. To overcome these, QA teams need to focus on vulnerabilities of the application under test, networks, integration to the ecosystem, and third-party software deployed for complete functionality. Usage of tools to simulate Man In The Middle (MITM) attacks helps QA teams identify and overcome any sources of vulnerability through countermeasures.

Building action-oriented QA dashboards need to extend beyond depicting quality aspects to addressing security, infrastructure, compliance, and privacy.

Scalability and distributed ownership

Monolithic architectures depend on vertical scaling to address increased application loads, while in a cloud setup, this is more horizontal in nature. Needless to say that in a cloud-based architecture, there is no limitation to application scaling. Performance testing in a cloud architecture need not consider aspects like breakpoint testing since they can scale indefinitely.

With SaaS-based models, the QA team needs to be mindful that the organization may own some components that require testing. Other components that require testing may be outsourced to other providers, and some of these providers may include cloud providers. The combination of on-premise components and others on the cloud by the SaaS provider makes QA complicated.

Reliability and Stability

This entirely depends on the needs of the organization. An Amazon that deploys 100,000 times a day – features and updates of its application hosted in cloud vis-a-vis an aircraft manufacturer that ensures the complete update of its application before its aircraft is in the air, have diverse requirements for reliability stability. Ideally, testing done for reliability should uncover four categories – what we are aware of and understand, what we are aware of but do not understand, what we understand but are not aware of, and what we neither understand nor are we aware of.

Initiatives like chaos testing aim to uncover these streams by randomly introducing failures through automated testing and scripting and seeing how the application reacts/sustains in this scenario.

QA needs to address the below in a hybrid cloud setup are –

  • What to do when one cloud provider goes down
  • How can the load be managed
  • What happens to disaster recovery sites
  • How does it react when downtime happens
  • How to ensure high availability of application

Changes in organization structure

Cloud-based architecture calls for development through pizza teams, smaller teams fed by one or two pizzas, in common parlance. These micro product teams have testing embedded in them, translating into a shift from QA to Quality Engineering (QE). The tester in the team is responsible for engineering quality by building automation scripts earlier in the cycle, managing performance testing strategies, and understanding how things get impacted in a cloud setup. Further, there is also increased adoption of collaboration through virtual teams, leading to a reduction in cross-functional QA teams.

Tool and platform landscape

A rapidly evolving tool landscape is the final hurdle that the QA practitioner must overcome to test a cloud-based application. The challenge becomes orchestrating superior testing strategies by using the right tools and the correct version of tools. Quick learning ability to keep up with this landscape is paramount. An open mindset to adopt the right toolset for the application is needed rather than an approach steeped with blinders towards toolsets prevailing in the organization.

In conclusion, the QA or QE team behaves like an extension of customer organization since it owns the mandate for ensuring the launch of quality products to market. The response times in a cloud-based environment are highly demanding since the launch time for product releases keeps shrinking on account of demands from end customers and competition. QA strategies for cloud-based environments need to keep pace with the rapid evolution and shift in the development mindset.

Further, the periodicity of application updates has also radically changed, from a 6-month upgrade in a monolith application to feature releases that happen daily, if not hourly. This shrinking periodicity translates into an exponential increase in the frequency of test cycles, leading to a shift-left strategy and testing done in earlier stages of the development lifecycle for QA optimization. Upskilling is also now a mandate given that the tester needs to know APIs, containers, and testing strategies that apply to contract-based components compared to pure functionality-based testing techniques.

Wish to know more? Feel free to reach out to us.

The Best Test Data Management Practices in an Increasingly Digital World

A quick scan of the application landscape shows that customers are more empowered, digitally savvy, and eager to have superior experiences faster. To achieve and maintain leadership in this landscape, organizations need to update applications constantly and at speed. This is why dependency on agile, DevOps, and CI/CD technologies has increased tremendously, further translating to an exponential increase in the adoption of test data management initiatives. CI/CD pipelines benefit from the fact that any new code that is developed is automatically integrated into the main application and tested continuously. Automated tests are critical to success, and agility is lost when test data delivery does not match code development and integration velocity.

Why Test Data Management?

Industry data shows that up to 60% of development and testing time is consumed by data-related activities, with a significant portion dedicated to testing data management. This amply validates that the global test data management market is expected to grow at a CAGR of 11.5% over the forecast period 2020-2025, according to the ResearchandMarkets TDM report.

Best Practices for Test Data Management

Any organization focusing on making its test data management discipline stronger and capable of supporting the new age digital delivery landscape needs to focus on the following three cornerstones.

Applicability:
The principle of shift left mandates that each phase in an SDLC has a tight feedback loop that ensures defects don’t move down the development/deployment pipeline, making it less costly for errors to be detected and rectified. Its success hinges to a large extent on close mapping of test data to the production environment. Replicating or cloning production data is manually intensive, and as the World Quality Report 2020-21 shows, 79% of respondents create test data manually with each run. Scripts and automation tools can take up most heavy lifting and bring this down to a large extent when done well. With production quality data being very close to reality, defect leakage is reduced vastly, ultimately translating to a significant reduction in defect triage cost at later stages of development/deployment.

However, using production-quality data at all times may not be possible, especially in the case of applications that are only a prototype or built from scratch. Additionally, using a complete copy of the production database is time and effort-intensive – instead, it is worthwhile to identify relevant subsets for testing. A strategy that brings together the right mix of product quality data and synthetic data closely aligned to production data models is the best bet. While production data maps to narrower testing outcomes in realistic environments, synthetic data is much broader and enables you to simulate environments beyond the ambit of production data. Usage of test data automation platforms that allocates apt dataset combinations for tests can bring further stability to testing.

Tight coupling with production data is also complicated by a host of data privacy laws like GDPR, CCPA, CPPA, etc., that mandate protecting customer-sensitive information. Anonymizing data or obfuscating data to remove sensitive information is an approach that is followed to circumvent this issue. Usually, non-production environments are less secure, and data masking for protecting PII information becomes paramount.

Accuracy:
Accuracy is critical in today’s digital transformation-led SDLC, where app updates are being launched to market faster and need to be as error-free as possible, a nearly impossible feat without accurate test data. The technology landscape is also more complex and integrated like never before, percolating the complexity of data model relationships and the environments in which they are used. The need is to maintain a single source of data truth. Many organizations adopt the path of creating a gold master for data and then make data subsets based on the need of the application. Adopting tools that validate and update data automatically during each test run further ensures the accuracy of the master data.

Accuracy also entails ensuring the relevance of data in the context of the application being tested. Decade-old data formats might be applicable in the context of an insurance application that needs historic policy data formats. However, demographic data or data related to customer purchasing behavior applicable in a retail application context is highly dynamic. The centralized data governance structure addresses this issue, at times sunsetting the data that has served its purpose, preventing any unintended usage. This also reduces maintenance costs for archiving large amounts of test data.

Also important is a proper data governance mechanism that provides the right provisioning capability and ownership driven at a central level, thereby helping teams use a single data truth for testing. Adopting similar provisioning techniques can further remove any cross-team constraints and ensure accurate data is available on demand.

Availability:
The rapid adoption of digital platforms and application movement into cloud environments have been driving exponential growth in user-generated data and cloud data traffic. The pandemic has accelerated this trend by moving the majority of application usage online. ResearchandMarkets report states that for every terabyte of data growth in production, ten terabytes are used for development, testing, and other non-production use cases, thereby driving up costs. Given this magnitude of test data usage, it is essential to align data availability with the release schedules of the application so that testers don’t need to spend a lot of time tweaking data for every code release.

The other most crucial thing in ensuring data availability is to manage version control of the data, helping to overcome the confusion caused by conflicting and multiple versioned local databases/datasets. The centrally managed test data team will help ensure single data truth and provide subsets of data as applicable to various subsystems or based on the need of the application under test. The central data repository also needs to be an ever-changing, learning one since the APIs and interfaces of the application keeps evolving, driving the need for updating test data consistently. After every test, the quality of data can be evaluated and updated in the central repository making it more accurate. This further drives reusability of data across a plethora of similar test scenarios.

The importance of choosing the right test data management tools

In DevOps and CI/CD environments, accurate test data at high velocity is an additional critical dimension in ensuring continuous integration and deployment. Choosing the right test data management framework and tool suite helps automate various stages in making data test ready through data generation, masking, scripting, provisioning, and cloning. World quality report 2020-21 indicates that the adoption of cloud and tool stacks for TDM has witnessed an increase, but there is a need for more maturity to make effective use.

In summary, for test data management, like many other disciplines, there is no one size fits all approach. An optimum mix of production mapped data, and synthetic data, created and housed in a repository managed at a central level is an excellent way to go. However, this approach, primarily while focusing on synthetic data generation, comes with its own set of challenges, including the need to have strong domain and database expertise. Organizations have also been taking TDM to the next level by deploying AI and ML techniques, which scan through data sets at the central repository and suggest the most practical applications for a particular application under test.

Need help? Partner with experts from Trigent to get a customized test data management solution and be a leader in the new-age digital delivery landscape.

Poor application performance can be fatal for your enterprise, avoid app degradation with application performance testing

If you’ve ever wondered what can possibly go wrong’ after creating a foolproof app, think again. Democrats’ Iowa Caucus voting app is a case in point. The Iowa caucus post-mortem pointed towards a flawed software development process and insufficient testing.

The enterprise software market revenue is expected to grow with a CAGR of 9.1% leading to a market volume of US$ 326,285.5 m by 2025. It is important that enterprises aggressively work towards getting their application performance testing efforts on track to ensure that all the individual components that go into the making of the app provide superior responses to ensure a better customer experience.

Banking app outages have also been pretty rampant in recent times putting the spotlight on the importance of application performance testing. Customers of Barclays, Santander, and HSBC suffered immensely when their mobile apps suddenly went down. It’s not as if banks worldwide are not digitally equipped. They dedicate at least 2-3 percent of their revenue to information technology along with additional expenses on building a superior IT infrastructure. What they also need is early and continuous performance testing to address and minimize the occurrence of such issues.

It is important that the application performs well not just when it goes live but later too. We give you a quick lowdown on application performance testing to help you gear up to meet modern-day challenges.

Application performance testing objectives

In general, users today, have little or no tolerance for bugs or poor response times. A faulty code can also lead to serious bottlenecks that can eventually lead to slowdown or downtime. Meanwhile, bottlenecks can arise from CPU utilization, disk usage, operating system limitations, or hardware issues.

Enterprises, therefore, need to conduct performance testing regularly to:

  • Ensure the app performs as expected
  • Identify and eliminate bottlenecks through continuous monitoring
  • Identify & eliminate limitations imposed by certain components
  • Identify and act on the causes of poor performance
  • Minimize implementation risks

Application performance testing parameters

Performance testing is based on various parameters that include load, stress, spike, endurance, volume, and scalability. Resilient apps can withstand increasing workloads, high volumes of data, and sudden or repetitive spikes in users and/or transactions.

As such, performance testing ensures that the app is designed keeping peak operations in mind, and all components comprising the app function as a cohesive unit to meet consumer requirements.
No matter how complex the app is, performance testing teams are often required to take the following steps:

  • Setting the performance criteria – Performance benchmarks need to be set and criteria should be identified in order to decide the course of the testing.
  • Adopting a user-centric approach – Every user is different and it is always a good idea to simulate a variety of end-users to imagine diverse scenarios and test for use cases accordingly. You would therefore need to factor in expected usage patterns, the peak times, length of an average session within the application, how many times do users use the application in a day, what is the most commonly used screen for the app, etc.
  • Evaluating the testing environment – It is important to understand the production environment, the tools available for testing, and the hardware, software, and configurations to be used before beginning the testing process. This helps us understand the challenges and plan accordingly.
  • Monitoring for the best user experience – Constant monitoring is an important step in application performance testing. It will give you answers to what, when, and why’ helping you fine-tune the performance of the application. How long does it take for the app to load, how does the latest deployment compare to previous ones, how well does the app perform while backend performances occur, etc. are things you need to assess. It is important that you leverage your performance scripts well with proper correlations, and monitor performance baselines for your database to ensure it can manage fresh data loads without diluting the user experience.
  • Re-engineering and re-testing – The tests can be rerun as required to review and analyze results, and fine-tune again if necessary.

Early Performance Testing

Test early. Why wait for users to complain when you can proactively run tests early in the development lifecycle to check for application readiness and performance? In the current (micro) service-oriented architecture approach, as soon as the component or an interface is built, performance testing at a smaller scale can allow us to uncover issues w.r.t concurrency, response time/latency, SLA, etc. This will allow us to identify bottlenecks early and gain confidence in the product as it is being built.

Performance testing best practices

For the app to perform optimally, you must adopt testing practices that can alleviate performance issues across all stages of the app cycle.

Our top recommendations are as follows:

  • Build a comprehensive performance model – Understand your system’s capacity to be ready for concurrent users, simultaneous requests, response times, system scalability, and user satisfaction. The app load time, for instance, is a critical metric irrespective of the industry you belong to. Mobile app load times can hugely impact consumer choices as highlighted in a study by Akamai which suggested conversion rates reduce by half and bounce rate increases by 6% if a mobile site load time goes up from 1 second to 3. It is therefore important that you factor in the changing needs of customers to build trust, loyalty, and offer a smooth user experience.
  • Update your test suite – The pace of technology is such that new development tools will debut all the time. It is therefore important for application performance testing teams to ensure they sharpen their skills often and are equipped with the latest testing tools and methodologies.

An application may boast of incredible functionality, but without the right application architecture, it won’t impress much. Some of the best brands have suffered heavily due to poor application performance. While Google lost about $2.3 million due to the massive outage that occurred in December 2020, AWS suffered a major outage after Amazon added a small amount of capacity to its Kinesis servers.

So, the next time you decide to put your application performance testing efforts on the back burner, you might as well ask yourself ‘what would be the cost of failure’?

Tide over application performance challenges with Trigent

With decades of experience and a bunch of the finest testing tools, our teams are equipped to help you across the gamut of application performance right from testing to engineering. We test apps for reliability, scalability, and performance while monitoring them continuously with real-time data and analytics.

Allow us to help you lead in the world of apps. Request a demo now.

Improve the quality of digital experiences with Performance Engineering

Quality at the heart of business performance

“In 2020, the key expectation is fast, reliable, and trustworthy software.” *

As businesses embrace the Agile/DevOps culture and the emphasis on CI/CD is growing, quality assurance is seen as a standalone task, limited to validating functionalities implemented. When QA and Testing is an afterthought in an Agile/DevOps culture, the result is a subpar consumer experience followed by an adverse impact on the revenue pipeline. Poor customer experience also directly impacts brand credibility and business equity. While UI/UX are the visible elements of the customer experience, product, or service performance is a critical element that is often neglected. Performance Testing identifies the gaps that are addressed through Performance Engineering.

Small steps, significant gains – the journey towards Performance Engineering

The deeper issue lies in the organization’s approach towards quality and testing – it is considered an independent phase rather than looked upon as a collaborative and an integrated approach. Performance engineering is a set of methodologies that identifies potential risks and bottlenecks early on in the development stage of the product and addresses them. It goes without saying that performance is an essential ingredient in the quality of the product, there’s a deeper need for change in thinking – to think proactively, anticipate early in the development cycle, test and deliver a quality experience to the end consumer. An organization that makes gradual changes in its journey towards performance engineering stands to gain significantly. The leadership team, the product management, and the engineering and DevOps at different levels need to take the shift-left approach towards performance engineering.

Make Performance Engineering your strategic priority today

Despite the obvious advantages, performance testing is typically a reactive measure that is addressed after the initial launch. However, organizations need to embrace performance engineering measures right from the design phase, start small, and take incremental steps towards change.

Covid-19 has rapidly changed the way consumers behave globally. Businesses caught onto remote working; consumers moved shopping, entertainment, banking, learning, and medical consultations online. Consider the quantum jump in usage triggered by the pandemic.

The dramatic increase in the use of digital services has covered decades in days.**

Companies that adopted scalability and performance centric design have moved swiftly to capture the market opportunity.

With multiple user-interfaces across sectors being the norm and the increasing complexity of digital experiences, it is critical for businesses to get it right the first time in order to gain and retain customers’ trust.

As cloud migrations continue, whether rehosting the app on an IaaS or rebuilding a new approach, performance engineering ensures that migrated systems withstand sudden surges in usage. According to a Sogeti and Neotys report, 74% of the load testing infrastructure is operated in the cloud today. Cloud infrastructure providers ensure reliability but they may not be aware of the performance metrics that matter to the business and their impact. As organizations move from monolithic systems to distributed architectures provided by an assortment of companies, corporate leaders need to recognize the importance of performance engineering and embrace it to deliver the right solutions for the first time.

Our approach to Performance Engineering philosophy

At Trigent, we put the customer experience at the heart of planning the entire testing cycle. Our performance engineering practices align with ‘metrics that matter’ to businesses in the DevOps framework. While testing identifies the gaps in performance, the onus of architecting it right lies on the DevOps engineering team with proactive inputs from QA and Testing.

Performance engineering is also a way of thinking, the ability to plan for performance at the time of design, right at the beginning. As for quality, besides testing for functionality, anticipating potential bottlenecks helps us assess the process in its entirety in the beginning.

Asking some of these customer-centric questions early on shifts the perspective right at the outset. Ask them early, and you’re on your way to a performance engineering culture.

Parameters that matter

‘Will my application meet the defined response-time requirements of my customers?’

Consider an app that doesn’t respond within the expected standards of the customer; the chances of that application making it to the customer’s phone screen is pretty slim.

‘Will the application handle the expected user load and beyond?’

An application that tested well with 10 users may fail when that number is multiplied by a thousand or two.

We take the viewpoints of multiple stakeholders, consider parameters that matter to the customer, and assess impact early on.

Customer experience matters

Performance Engineering takes into account the overall experience of the end-user and their environment.

Asking pertinent questions such as ‘Will my users experience acceptable response times, even during peak hours?’ or ‘Does the application respond quickly enough for the intended users?’ does well to anticipate potential pitfalls in network usage and latency.

‘Where are the bottlenecks in my multi-user environment?’

Understand the real environment of the user and their challenges to provide a quality user experience.

Early Focus

The non-functional aspects are integrated into the DevOps and an early focus on performance enables us to gain insights into architectural issues.

‘How can we optimize the multi-user application before it goes live?
‘How can we detect errors that only occur under real-load conditions?

Quick course corrections help optimize performance and make the product market-ready. Besides faster deployment, quality assurance gives our clients an added advantage of reduced performance costs.

Architect it right

‘What system capacity is required to handle the expected load?’
‘Will the application handle the number of transactions required by the business?’

Important questions like these focus on architecting the product for performance. As part of the performance engineering methodology, our teams consistently check and validate the capabilities at the time of developing the product or scaling it up. We take the shift-left and shift-right approach to anticipate, identify, and remove bottlenecks early on. Getting the architecture right enables us to deliver and deploy a high-quality product every time.

Performance engineering done right is sure to improve the planning-to-deployment time with high-quality products. Plus, it reduces performance costs arising out of unforeseen issues. A step-by-step approach in testing makes sure organizations move towards achieving performance engineering. Talk to our experts for scalable performance engineering solutions for your business.

Learn more about Trigent software testing services.


Reference:
* The State of Performance Engineering 2020 – A Sogeti and Neotys report
** Meet the next-normal consumer – A McKinsey & Company report

Accelerate CI/CD Pipeline Blog Series – Part II – Test Automation

In part I of this blog series we spoke about Continuous Testing (CT), CI CD and that Test Automation is a key to its success, how to leverage test automation to enable coverage and speed. Let’s get an in-depth understanding of why it’s essential.

Why Automation of Testing is Essential for CI CD

Code analysis tools, API testing tools, API contract testing tools, Service virtualization tools, performance assessment tools, and end-to-end automation tools are all parts of CT. However, test automation is one of the key enablers of CT as:

  • It allows us to execute tests in parallel, across multiple servers/containers, speeding up the testing process.
  • It frees engineers from repetitive tasks, enabling them to focus on value-adds.
  • It helps validate the impact of even minor changes continuously.

Characteristics of a Good Automation Framework

For test automation to be effective, it must be supported by efficient frameworks. The frameworks provide a structure and help integrate and realize the efforts of a distributed team.

  • Test frameworks must support various languages, browsers, and techniques to make them future-proof. They must possess an Agile testing environment to support the continuous delivery pipeline.
  • The testing platform must be scalable to support as many tests as needed.
  • The platform must support compatibility tests on simulated devices to cut down the cycle.
  • The environment should maximize automation, i.e., trigger tests, analyze results, and share test information across the organization, in a fully automated manner.
  • The testing platform must include security features such as encryption of test data and access control policies

Characteristics of Trigent’s AutoMate Test Automation Framework

Reinforced by globally renowned partners, Trigent’s testing focus is aligned with the current business environment and offers cost benefits, performance, and agility.

As niche test automation experts, we have significant experience in open source and commercial testing tools. Our extensive library of modular, reusable, and resilient frameworks simplifies scenario-based automation. We provide on-demand testing and next-gen scheduling.

Features and benefits
  • Accelerated script development: Script/test cases development effort reduced up to 60-80% in comparison to traditional test automation approaches. Reduces Test Scripting Complexity.
  • Modular and reusable framework components: Reduced dependency on tool-specific resources. Ability to kick-start automation quickly. It supports reusable components. Reduced Test Automation maintenance costs. Allows multi-browser, multi-device testing.
  • Easy test script maintenance: Ease of test execution. Easy to make changes and maintain scripts in the long run. Improved error and exception handling. Supports multiple scripting languages.
  • On-demand Testing: Sanity, Smoke, Integration, Regression, etc. Provides effective test data creation on the go.
  • Hybrid Model: Modular test framework built using JUnit or TestNG. Integrates with multiple test automation tools. Allows a common way of handling multiple test types.
  • Scheduling and customized reporting: Send test results to ALM. Integrates with Test Management tools to track your test plans effectively.
  • Leverages new tech trends: Leverages AI/ML utilities & tools that allow for effective Test Impact analysis and test selection. Integrates with CI/CD tools to enable automated executions. Allows parallel executions to reduce test execution time

Learn more about Trigent software testing services or test automation services

Accelerate CI/CD Pipeline Blog Series – Part 1- Continuous Testing

Given its usefulness in software development, Agile methodologies have come to be embraced across the IT ecosystem to streamline processes, improve feedback, and accelerate innovation.

Organizations now see DevOps as the next wave after Agile that enables Continuous Integration and Continuous Delivery (CI/CD).  While Agile helped streamline and automate the entire software delivery lifecycle, CI/CD goes further. CI checks the code often, and the tested chunks are integrated, sometimes several times in a single day, to create a stream of smaller and frequent releases through CD.

As a principal analyst at Forrester Research puts it succinctly: ”If Agile was the opening act, continuous delivery is the headliner. The link that enables CI/CD, however, is Continuous Testing (CT).

What is Continuous Testing?

Continuous Testing is a process by which feedback on business risks of a software release is acquired as rapidly as possible. It helps in early risk identification & incremental coverage as it’s integrated into the delivery pipeline. Continuous Testing is achieved by making test automation an integral part of the software delivery pipeline. It’s seamlessly interwoven into the software delivery pipeline (not tagged at the end).

Though CI/CD enables speed-to-market, inadequate end-to-end experience testing can turn it into a liability.  A key aspect of CT is to leverage test automation to enable coverage and speed.

Test Automation – Continuous Testing’s Secret Success Factor

Automation of tests is the key to ensure that Quality Assurance is as continuous, agile, and reliable.  CT involves automating the tests and running them early and often.  It leverages service virtualization to increase the test coverage when parts of the business functions are available at different points in time.

Automated Testing binds together all the other processes that comprise the CD pipeline and makes DevOps work. By validating changing scenarios, Smart automation helps in faster software delivery.

In part II of the blog series we will talk more about why test automation is essential for CI/CD Testing, and automation framework.

Learn more about Trigent software testing services or test automation services

Can Machine Learning Power the Future of Software Testing?

Machine learning in software testing

Software testing professionals today are under immense pressure to make faster risk-based, go-live decisions, what with DevOps practices having shrunk the time to deliver test results. What was expected every two weeks is now expected umpteen times in a day.

The job of a tester has also become more demanding due to increasing complexities in applications. Testers are now expected to deliver a go/no go decision that compliments fast-paced development and deployment.

How to use machine learning in software testing

A recent piece announcing the launch of a data-driven ML-powered engine to assist testers sounds promising, but will it deliver on the promise?

Kawaguchi’s reasoning behind his new venture is based on the way different industries have benefited by using data to drive processes and efficiencies. He opines that the same can be replicated in the software industry, specifically to the practice of software testing.

Through Launchable, Kawaguchi plans to utilize machine learning to help provide quantifiable indicators that help testers perform risk-based testing and get a clear understanding of the quality and impact of the software when ready for deployment.

A machine learning engine is expected to predict test cases that could fail due to changes in the source code. Knowing in advance about test cases that are poised for failure would allow testers to run a subset of tests in an order that would minimize the feedback delay.

Our view on the use of machine learning in software testing

As testers, skeptics we are!!

Whilst there is no doubt that time to deliver has become a significant constraint for testers and automation helps to speed things up, the selection of tests to automate is still an expert-driven process.

When the quantum of changes are small and changes localized, we may probably be able to have an AI algorithm, that through a reduced set of features, can arrive at an intelligent risk assessment.

However, as testers, we have also seen that a small quantum of changes can result in a large regression impact. In this case, the feature set we may need to assess may be insufficient.

What if the quantum of change is large. The features that the algorithm needs to consider may not be limited to code alone, but also depend on a lot of external factors, including business considerations to drive the test focus decision. That makes the data points required for decision making, sizable.

To date, the ability of AI to replace human instinct and interplay is yet to be proven!

Until such a time, that one understands the features considered to assess risk. The biases that the algorithm may have absorbed while being trained. And that AI can replace critical thinking is proven – this will be one more output that will need to get assessed for the possible risk it can pose to the decision-making process.

Kosuke Kawaguchi is confident about his approach. That’s the claim he made when he announced the launch of his startup. We have eagerly signed up for a beta here, and will keenly observe the impact that these set of AI algorithms have on software testing.

Here’s to more innovations in this sphere!!

Learn more about Trigent software testing services or functional testing services.

Outsourcing Testing in a DevOps World

Software products today are being developed for a unified experience. Applications are created to perform and deliver a seamless experience on multiple types of devices, operating on various platforms.

Additionally, the growing demand for launching products at pace and scale is pushing businesses towards ensuring that they are market-ready in shorter time frames. The prevalence of Agile/DevOps practices now requires testing to be carried out simultaneously to development. Continuous development, integration, testing, and deployment have become the norm. Testers are now a part of the development process, testing the features, releases, and updates in parallel as they get developed.

The testing & deploying of a multi-platform product in a fast-paced environment requires expertise and complimenting infrastructure to deliver a unified experience. Add multiple product lines, constant updates for new features, a complex deployment, a distributed user base, into the mix, and your search for an outsourcing partner could become a daunting task.

We share some considerations that can guide your decision making — drawn from our experience of working as outsourcing partners for some of our clients, helping them deliver quality products on time.

Criteria our clients applied before selecting us as their outsourcing testing partners

Need for staff augmentation vs. managed services

You can choose staff augmentation if the requirement is short term and the tasks are well defined. In the case of a long term project, it is best to opt for managed services. Managed services suit best if the project requires ongoing support and skill sets that are not available with the business but are vital for the product or project. It also fits well for long term projects that have a clear understanding of outputs and outcomes.

Agility of the vendor’s testing practices

Agile/DevOps methodologies now drive a healthy percentage of software development and testing. Can the vendor maintain velocity in an Agile/DevOps environment? Do they have the processes to integrate into cross-functional, distributed teams to ensure continuous integration, development, testing, and deployment?

Relevant experience working for your industry

Relevant industry experience ensures that the testers involved know about your business domain. Industry knowledge not only increases efficiency but also guides testers to prioritize testing with the highest level of business impact.

Tools, frameworks, and technologies that the vendor offers

Understand the expertise of the vendor in terms of the tools, frameworks, and technologies. What is their approach to automation? Do they use/recommend licensed or open source tools? These are some considerations that can guide your evaluation.

Offshoring – Onshoring – Bestshoring

Many vendors recommend offshoring processes to reap benefits from cost savings. But does offshoring translate to an equally beneficial proposition for you? While you can best ascertain the applicability and benefits of offshoring, it is advisable to go for a mix of the three. In a managed services engagement, right shoring (a mix of onsite & offshore), ensures that the coordination aspects are dealt with by the vendor.

Reputation in the market

Ascertaining the reputation of the vendor in the market can be another useful way of evaluation. Reading independent reviews about the organization, understanding the longevity of their existing engagements (Customer stickiness), references from businesses that have engaged with the vendor earlier, number of years the organization has been in business are some of the factors that can be applied.

Culture within the organization

The culture of your potential partner must be broadly aligned with your organizational culture. The vendor should identify with your culture, be quick to adapt, be ethical, and gel well with the existing team. The culture within the vendor organization is also crucial to ensure that the employees are respectful of their commitments and stay accountable for assigned responsibilities.

Low-cost vs. high-quality output

Engaging with people who have the experience in the required domain and technology, working on a customized delivery model, within stipulated timelines, come for a premium. But are more likely to deliver value compared to a low-cost fragmented solution with inexperienced manpower, little or zero knowledge on domain-specific skills and technology, and an unsure commitment to timely delivery.

Do you know of other factors that influence decision making in terms of identifying the right outsourcing partner? Please share your thoughts.

Can your TMS Application Weather the Competition?

The transportation and logistics industry is growing dependent on diverse transportation management systems (TMS). This is true not only for the big shippers but also for small companies triggered by different rates, international operations, and competitive landscape. Gartner’s 2019 Magic Quadrant for Transportation Management Systems summarizes the growing importance of TMS solutions when it says, “Modern supply chains require an easy-to-use, flexible and scale TMS solution with broad global coverage. In a competitive transportation and logistics environment, TMS solutions help organizations to meet service commitments at the lowest cost.

For TMS solution providers, the path to developing or modernizing applications is not as simple as cruising calm seas. Their challenges are myriad and relate to ensuring systems that organize quotes seamlessly (no jumping from phone to a website). They need to help customers to select the ideal carrier based on temperature, time, and load to ensure maximized benefits. Very importantly, they need to help customers to track shipments while managing multiple carrier options and freight. Customers look for answers, and TMS solutions should be able to provide customers the best options in carriers. All this does not come easy and while developing and executing the solution is half of it, the more critical half lies in ensuring that the system’s functionality, security, and performance remain uncompromised. When looking for a TMS solution, customers look for providers who can present a clear picture of the total cost of ownership. Unpredictability is a no-no in this business which essentially means that the solution is implemented and tested for 100 percent performance and functionality.

Testing Makes the Difference

The TMS solution providers who will be able to sustain their competitive edge are the ones who have tested their solution from all angles and are sure of its superiority.

In a recent case study that explains the importance of testing, a cloud-based trucking intelligence company provides solutions to help fleets improve safety and compliance while reducing costs invested in a futuristic onboard telematics product. The product manages several processes and functions to provide accurate and real-time information such as tracking fleet vehicles, controlling unauthorized access to the company’s fleet assets, and mapping real-me vehicle location. The client’s customers know more about their trucks on the road using pressure monitoring, fault code monitoring, and remote diagnostics link. The onboard device records and transmits information such as speed, RPMs and idle time, distance traveled, etc. in real-time to a central server using a cellular data network.

The data stored in the central server is accessed using the associated web application via the internet. The web application also provides a driver portal for the drivers to know/edit their hours of service logs. Since the system deals with mission-critical business processes, providing accurate and real-time information is key to its success.

The challenge was to set up a test environment for the onboard device to accurately simulate the environment in the truck and simulate the transmission of data to the central server. Establishing appropriate harnesses to test the hardware and software interface was equally challenging. The other challenges were the simulation and real-time data generation of the vehicle movement using a simulator GPS.

A test lab was set up with various versions of the hardware and software and integration points with simulators. With use-case methodology and user interviews, test scenarios were chalked out to test the rich functionality and usage of the device. Functional testing and regression testing of new releases for both the onboard equipment and web application were undertaken. For each of the client’s built-in products, end-to-end testing was conducted.

As a result of the testing services, the IoT platform experienced shortened functional release cycles. The comprehensive test coverage ensured better GPS validation, reduced preventive cost by identification of holistic test cases, reduced detection cost by performing pre-emptive tests like integration testing.

Testing Integral to Functional Superiority for TMS 

As seen in the case study above, developing, integrating, operating, and maintaining a TMS is a challenging business. There are several stakeholders and a complex process that includes integrated hardware, software, humans, and processes performing myriad functions, making the TMS’s performance heavily reliant on its functioning. Adding complexity is the input/output of data, command, and control, data analysis, and communication. As a result of its complexity and the importance of its functioning in managing shipping and logistics, testing is an essential aspect of a TMS.

Testing TMS solutions from the functional, performance design and implementation aspect will ensure that:

  • Shipping loads are accurate, and there are no unwelcome surprises
  • Mobile status updates eliminate human intervention and provide real-time updates.
  • Electronic record management to ensure the workflow is smooth and accurate
  • Connectivity information to eliminate issues with shift changes and visibility
  • API integration to seamlessly communicate with customers.
  • Managing risk for both the TMS and the system’s partners/vendors.

TMS software providers need to offer new features and capabilities faster to be competitive, win more customers, and retain their business. Whether it relates to seamless dispatch workflows, freight billing or EDI, Trigent can help. Know more about Trigent’s Transportation & Logistics solutions

Five Business Benefits of On-Demand Testing

Constantly shifting economic conditions has resulted in businesses tightening their IT budgets to control costs and remain competitive. However, while budgets are limited, expectations from IT managers are only growing larger. Most companies today have an online presence, and they need to frequently upgrade and innovate their offerings to stay competitive. This has led to new features, apps, and products being unleashed at a rapid space. There is also an uncompromising world of usability that requires released products and apps to be fault-free. However, the bottom line is, budgets remain controlled.

On-demand testing or Testing as a Service (TaaS) is a realistic option for stringent budgets and tight deadlines. Reliable service providers, who offer on-demand testing, have the capabilities for testing in cloud-based or on-premise environments. More often than not, these providers have a wide array of tools, assets, frameworks, and test environments.

End-to-end testing services that you need on on-demand basis.

On-demand testing is offered by companies that are confident of taking on the responsibility of transferred ownership. For QA and IT managers, the risks attached to testing and the costs for tools can be assigned to service providers thereby immediately diminishing both risk and added expenditures.

Unlike other services, on-demand testing is demanding in its expectations. Those offering this service cannot afford to escape, bugs, slipped deadlines, and, therefore, the advantages far outweigh the effort involved in sourcing the right partner.

Some of the benefits of On-Demand Testing are:

1. As costs are negotiated and finalized with the partner, the probability of unexpected expenses is brought down considerably. There is a commitment agreed upon and that helps in better budget allocation. There are instances where clients have experienced over 50 percent savings in costs. However, the savings are dependent on the choice of the service provider.

2. Dynamic and scalable testing requirements benefit from on-demand testing, where the partner’s team can depute several test engineers on to the job, reducing testing time drastically. For example, imagine a small number of 3-5 in house engineers who are caught up in multiple projects, to a dedicated team of any number of test engineers focused on a single project. The advantage in terms of time spent can itself become a key advantage.

3. On-demand testing is especially useful for load, performance, and last-mile testing where real-life scenarios need to be created. With real-life test environments at their disposal, service providers are capable of maximizing test coverage and results in limited time frames.

4. Time-to-market can be reduced when partners are involved, as they will help to plan and schedule test cycles without any delays. In such cases, the saved time can be at least 30 percent.

5. Reliable on-demand testing service providers offer standardized infrastructure, frameworks, and pre-configured environments to ensure that configuration errors do not creep in after release.

Trigent’s SwifTest is a pay-as-you-go software testing service that is best suited to gain instant access to qualified professional software testers without any long-term contracts. Trigent will perform end-to-end functional testing of your web or mobile applications. Our service is offered on inclusive environments of mobile devices, operating systems, and browsers – to help you validate your product at a pace faster than traditional outsourced testing service. Our certified testing specialists will ensure all user functions (links, menus, buttons, etc.) are working properly on target devices and browsers. We’ll perform exploratory testing and follow your specified test steps. This Development-QA follow-the-sun model reduces project duration and increases responsiveness. You pay only for the time that you engage our QA engineers/team-as little as one day (8 hours) / a week / or a month.
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