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 is predicted to touch US$230,134.0m in 2021, and the revenue is expected to grow with a CAGR of 9.1% leading to a market volume of US$326,285.5m 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.

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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Performance testing on cloud

Ensuring good application performance is crucial, especially to critical web applications, that require fast cycle times and short turnaround times for newer versions. How can such applications be optimally tested without spending a fortune on tools, technology, and people but still ensure quality assurance and on-time release? With tightening budgets and time-consuming processes, IT organizations are forced to do more with less.

A judicious combination of tools, the available cloud platforms, and well thought out methodology provide us the answer. While it is proven that open source software can reduce software development cost, there hasn’t been much use of open source tools for testing until the cloud computing paradigm has become more readily available. Until now performance testing on a large-scale testing project using test tools on dedicated hardware to model real-world scenarios has been an expensive proposition. However, cloud testing has changed the game.

Performance testing on the cloud can be broadly classified into two categories:

  1. Cloud Infrastructure for Test Environment – Performance testing always requires some sophisticated tool infrastructure. The test infrastructure requirement could vary from having specific hardware for specific tools, the number of hardware, licenses, back-ups, Bandwidth, etc. In the past, getting all the required hardware was not only challenging but also in many cases, the performance testing was not adequately tested due to missing test tools. With Cloud testing, one can just focus on performance testing and simply not on the infrastructure. Any tool be it open source like Grinder, JMeter, or any licensed software products like Silk Test Performer can be easily set up and run the test on the AUT (Application Under Test). There is some time required for setting up the tool and also requires few test runs to ensure the load injectors (the client machine that generates load) do not cause bottlenecks. This environment may be best suited in a typical waterfall model scenario where the software is evaluated and tuned at the end of the software development cycle.
  1. Cloud as a Test Tool – There are different sets of software testing tools that are readily available in the Cloud as a SaaS model. The test tool is readily available on the cloud, therefore no setup required, just subscribe and you are all set to go thus saving time in setup. Also, their system configuration is optimized to generate the required load without causing the bottlenecks. Some of the readily available test tools on Cloud are – LoadStorm, CloudTest by SOASTA, BrowserMob, nGrinder, Load-Intelligence (Can Use JMETER in Cloud), etc. This environment is more suited in an Agile scenario, where the same tasks need to be performed for smaller iterations from the initial stage of the SDLC itself. So, here you just have the scripts ready and upload to the cloud run the test and once you have the requirement metrics, you sign-off.

Conclusion – A combination of carefully selected testing tools, QA testers, readily available cloud platforms, and a sound performance test strategy, can make bring the same benefits as of the conventional methods at a much lower cost.