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.

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Outsourcing QA in the world of DevOps – Best Practices for Dispersed (Distributed) QA teams

DevOps is the preferred methodology for software development and release, with collaborating teams oriented towards faster delivery cycles augmented by early feedback. QA is a critical binding thread of DevOps practice, with early inclusion at the story definition stage. Adoption of a distributed model of QA had earlier been bumpy, however, the pandemic has evened out the rough edges.

The underlying principle which drives DevOps is collaboration. With outsourced QA being expedited through teams distributed across geographies and locations, a plethora of aspects that were hitherto guaranteed through co-located teams, have now come under a lot of pressure. Concerns range from constant communication and interworking to coverage across a wide range of testing types – unit testing, API testing as well as validating experiences across a wide range of channels. As with everything in life, DevOps needs a balanced approach, maintaining collaboration and communication between teams while ensuring that delivery cycles are up to speed and the quality of the delivered product meets customer expectations.

Outlined below some of the best practices for ensuring the effectiveness of distributed QA teams for an efficient DevOps process.

Focus on right capability: While organizations focus to a large extent on bringing capabilities across development, support, QA, operations, and product management in a scrum team, paramount from a quality perspective would be QA skills. The challenge is to find the right skill mix. For example, a good exploratory tester; good automation skills (not necessarily in the same person). In addition, specialist skills related to performance, security, accessibility also need to be thought through. The key is to choose an optimum mix of specialists and generalists.

Aim to achieve the right platform/tool mix: It is vital to maintain consistency across the tool stacks used for engagement. As per a 451 research survey, 39% of respondents juggle 11 to 30 tools so as to keep an eye on their application infrastructure and cloud environment; 8% are even found to use over 21 to 30 tools. Commonly referred to as tool sprawl, this makes it extremely difficult to collaborate in an often decentralized and distributed QA environment. It’s imperative to have a balanced approach towards the tool mix, ideally by influencing the team to adopt a common set of tools instead of making it mandatory.

Ensure a robust CI/process and environment: A weak and insipid process may cause the development and operations team to run into problems while integrating new code. With several geographically distributed teams committing code consistently into the CI environment, shared dev/test/deploy environments constantly run into issues if sufficient thought process has not gone into identification of environment configurations. These can ultimately translate into failed tests and thereby failed delivery/deployment. A well-defined automated process ensures continuous deployment & monitoring throughout the lifecycle of an application, from integration and testing phases through to release & support.

A good practice would be to adopt cloud-based infrastructure, reinforced by mechanisms for managing any escalations on deployment issues effectively and quickly. Issues like build fail or lack of infra support can hamper the productivity of distributed teams. When strengthened by remote alerts and robust reporting capabilities for teams and resilient communication infrastructure, accelerated development to deployment becomes a reality.

Follow good development practices: Joint backlog grooming exercises with all stakeholders, regular updates on progress, code analysis, and effective build & deployment practices, as well as establishing a workflow for defect/issue management, are paramount in ensuring the effectiveness of distributed DevOps. Equally important is the need to manage risk early with ongoing impact analysis, code quality reviews, risk-based testing, and real-time risk assessments. In short, the adoption of risk and impact assessment mechanisms is vital.
Another key area of focus is the need to ascertain robust metrics that help in the early identification of quality issues and eases the process of integration with the development cycle. Recent research from Gatepoint and Perfecto surveyed executives from over 100 leading digital enterprises in the United States on their testing habits, tools, and challenges. The survey results show that 63 percent start to test only after a new build and code is being developed. Just 40 percent test upon each code change or at the start of new software.

Devote equal attention to both manual and automation testing: Manual (or exploratory) testing allows you to ensure that product features are well tested, while automation of tests (or as some say checks!) helps you with improving coverage for repeatable tasks. Planning for both during your early sprint planning meetings is important. In most cases, automation is usually given step-motherly treatment and falls at the wayside due to scope creep and repeated testing due to defects. A 2019 state of testing report, shows that only 25 percent of respondents claimed they have more than 50 percent of their functional tests automated. So, the ideal approach would be to separate the two sets of activities and ensure that they both get equal attention from their own set of specialists.

Early non-functional focus: Organizations tend to overlook the importance of bringing in occasional validations of how the product fares around performance, security vulnerabilities, or even important regulations like accessibility, until late in the day. In the 2020 DevSecOps Community Survey, 55 percent of respondents deploy at least once per week, and 18 percent claim multiple daily deployments. But when it comes to security, 45 percent of the survey’s respondents know it’s important but don’t have time to devote to it. Security has a further impact on CI/CD tool stack deployment itself as indicated by the 451 research in which more than 60% of respondents said a lack of automated, integrated security tools is a big challenge in implementing CI/CD tools effectively.

It is essential that any issue which is non-functional in nature be exposed and dealt with before it moves down the dev pipeline. Adoption of a non-functional focus depends to a large extent on the evolution of the product and the risk to the organization.

In order to make distributed QA teams successful, an organization must have the capability to focus in a balanced and consistent way across the length and breadth of the QA spectrum, from people and processes to technology stacks. It is heartening to note that the recent pandemic situation has revealed a positive trend in terms of better acceptance of these practices. However, the ability to make these practices work, hinges on the diligence with which an organization institutionalizes these best practices as well as platforms and supplements it with an organizational culture that is open to change.

Trigent’s experienced and versatile Quality Assurance and Testing team is a major contributor to the successful launch, upgrade, and maintenance of quality software used by millions around the globe. Our experienced responsible testing practices put process before convenience to delight stakeholders with an impressive industry rivaled Defect Escape Ratio or DER of 0.2.

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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

Trigent excels in delivering Digital Transformation Services: GoodFirms

GoodFirms consists of researched companies and their reviews from genuine, authorized service-buyers across the IT industry. Furthermore, the companies are examined on crucial parameters of Quality, Reliability, and Ability and ranked based on the same. This factor helps customers to choose and hire companies by bridging the gap between the two.

They recently evaluated Trigent based on the same parameters, after which they found the firm excels in delivering IT Services, mainly:


Keeping Up with Latest Technology Through Cloud computing

Cloud computing technology has made the process of meeting the changing demands of clients and customers. The companies who are early adopters of the changing technologies always achieve cutting-edge in the market. Trigent’s cloud-first strategy is made to meet the clients’ needs by driving acceleration, customer insight, and connected experience to take businesses to the next orbit of cloud transformation. Their team exhibits the highest potential in cloud computing that improves business results across the key performance indicators (KPIs). The Trigent team is instilled with productivity, operational efficiency, and growth that increases profitability.

The team possesses years of experience and works attentively in the cloud adoption journey of their clients. The professionals curate all their knowledge to bring the best of services to the table. This way, the clients can seamlessly achieve goals and secure their place as a modern cloud based-enterprise. Their vigorous effort has placed them as the top cloud companies in Bangalore at GoodFirms website.

Propelling Business with Software Testing

Continuous efforts and innovations are essential for businesses to outpace in the competitive market. The Trigent team offers next-gen software testing services to warrant the delivery of superior quality software products that are release ready. The team uses agile – continuous integration, continuous deployment – and shift-left approaches by utilizing validated, automated tools. The team expertise covers functional, security, performance, usability, accessibility testing that extends across mobile, web, cloud, and microservices deployment.

The company caters to clients of all sizes across different industries. The clients have also sustained substantial growth by harnessing their decade-long experience and domain-knowledge. Bridging the gap between companies and customers and using agile methodology for test advisory & consulting, test automation, accessibility assurance, security testing, end to end functional testing, performance testing the company holds expertise in all. Thus, the company is dubbed as the top software testing company in Massachusetts at GoodFirms.

Optimizing Work with Artificial Intelligence

Artificial intelligence has been the emerging technology for many industries during the past decade. AI is defining technology by taking it to a whole new level of automation where machine learning, natural language process, and neural networks are used to deliver solutions. At Trigent, the team promises to support clients by utilizing AI and providing faster, more effective outcomes. By serving diverse industries with complete AI operating models – strategy, design, development, and execution – the firm is automating tasks. They are focused on empowering brands by adding machine capabilities to human intelligence and simplifying operations.

The AI development teams at Trigent are appropriately applying the resources to identify and govern a process that empowers and innovate business intelligence. Besides, with their help with continuous processes enhancements and AI feedback systems, many companies have been increasing productivity and revenues. Therefore, helping clients to earn profit with artificial intelligence, the firm would soon rank in the list of the artificial intelligence programming company at GoodFirms.

About GoodFirms

GoodFirms, a maverick B2B Research and Reviews Company helps in finding Cloud Computing, Testing Services, and Artificial Intelligence firms rendering the best services to its customers. Their  extensive research process ranks the companies, boosts their online reputation and helps service seekers pick the right technology partner that meets their business needs.