#devOpscontinuoustesting

[incight]

What Drives Continuous Testing in DevOps?

The need to deliver quality at speed by leveraging flexible delivery models with quick feedback loops has become critical in today’s dynamic business environment. This is where DevOps incorporating CI/CD and test automation can enable businesses to quickly adapt to changing market scenarios. With digital transformation being the buzzword to enhance efficiencies and competitiveness of businesses, DevOps continuous testing has become an essential element in the transition. It helps to minimize any associated business risks and delivers superior-quality products with quick turnarounds. Let us understand the need for continuous testing in the SDLC.

Continuous testing and its importance

Quality has become the cornerstone for business enterprises to achieve customer acceptance and increase brand visibility. The traditional software development approach of testing the integrated modules of a software application after development does not make it entirely free of bugs or vulnerabilities. However, the continuous testing approach of testing early and consistently throughout the SDLC using automation can mitigate the risks and improve the quality of the product. It is important for the following reasons:

Reduces the cost of rework by detecting and fixing bugs early

Enables developers to build features quickly by automating the QA process

Speeds up the testing process as continuous test automation tools reduce manual testing

Improves test coverage as automated tests are executed quickly and evaluate all features of the software application

Enables quicker and frequent release of quality applications

Brings consistency in testing as the configured environment allows the execution of comparative tests

Enhances the value of the software, allows accurate reporting of the errors, failures, and successes of the tests, and creates greater visibility for the stakeholders (developers and testers)

Facilitates coordination among teams with quick feedback and reduces the time for code review

The key to successful continuous testing in DevOps 

DevOps continuous testing is implemented in the following ways:

Develop a correct test code: A continuous testing strategy entails building the right test script to generate accurate test results. In other words, it would mean the difference between a high-value test suite and a wasteful and erroneous one. Testers should treat the test suite the same way they treat the production code.

Plan and execute a risk-based test automation strategy: User experience can determine whether a software application will be a success or otherwise in the market. Hence, issues leading to bad user experiences need to be identified and addressed. This calls for employing a risk-based DevOps testing approach to detect, evaluate, and mitigate the issues surrounding bad user experiences.

Provide fast feedback: Continuous testing services generate vast amounts of data that provide stakeholders (testers and developers) with deep and fresh insights into issues. These also provide the management with dashboard-based intelligence or perspective to choose “go or no go” for individual features.

Maintain test data: The DevOps approach to testing can generate mission-critical test data applicable to real-world scenarios. This data can help QA experts develop insights into the need for updating the application based on the evolving scenarios, customer preferences, and cost considerations.

Access to a stable test environment: To ensure the optimal performance of the test suite, it must be backed by a stable continuous testing framework. Further, the test environments, APIs, and third-party tools should be made available to the testers without downtime or latency.

Better collaboration between developers and testers: In the DevOps scheme of things, all silos must disappear and there should be enhanced collaboration between processes (development, testing, and operations). The teams should be small in size, and all test reports should be centrally located to be accessed by the stakeholders.

Prioritize automation: Instead of removing manual testing, automation should be prioritized across processes. Thus, focus on processes that need to run repeatedly by mapping out the SDLC and identifying the opportunities for automation.

Small design: Run iterative tests on small increments that are easier to design. Small designs allow easy automation and ready deployment.

Track everything: Set up test cases with the proper metrics and pass-fail criteria. Continuous testing can identify if there are issues with the code or if things are working or not.

Use the right tools: For continuous testing, get the right tools that can run in the working environment. Use tools that have community-based support to get better use cases and solutions.

Conclusion

Continuous testing allows the execution of automated tests in the SDLC to obtain immediate feedback on errors, bugs, vulnerabilities, or business risks. It facilitates the delivery of quality software applications seamlessly.

Resource

James Daniel is a software Tech enthusiastic & works at Cigniti Technologies. I'm having a great understanding of today's software testing quality that yields strong results and always happy to create valuable content & share thoughts.

Article Source: dev.to

[vikram739]

What does Test Orchestration Imply? What are its Benefits?

In the DevOps approach of developing, testing, and delivering software applications, there is a need to accelerate testing within the constraints of budget and time. This entails automating the testing process as part of the testing strategy. In many cases, continuous test automation is often considered as a discrete step in the build pipeline rather than a sequence of steps. For any continuous testing strategy, all software applications need to pass through a series of tests before they pass muster in the crucible of quality. These may include unit testing, integration testing, functional testing, smoke testing, performance testing, security testing, and others. For DevOps QA testing, each of these tests should be subjected to automation to enhance quality, reduce test time, and improve the certainty of software behavior.

However, merely automating the tests left, right, and center does not augur well when it comes to achieving an optimal quality level as well as minimizing the time and costs. In the ultimate analysis, what is needed is test orchestration. In fact, implementing test orchestration can be quite rewarding given that it accelerates DevOps continuous testing and offers a holistic view of the entire testing mechanism. Interestingly, when we talk about DevOps and the ways to enhance software quality, terms such as test orchestration and automation are thrown around randomly. This can be confusing for many stakeholders not in the know.

So, is there a difference between test automation and test orchestration? The answer is yes, as test orchestration allows DevOps teams to optimize the whole test process by scheduling the execution of automated tests in a particular sequence. It is a tool to bring about efficiency and integrates both manual and automated testing. Let us discuss test orchestration and its overarching benefits in the segments below.

What is test orchestration?

Test orchestration is the scheduling of a set or sequence of automated test activities in a well-defined order to be executed one after the other. Here, the tests are executed in a linear order, and the decision to control the order of testing vests with the DevOps team. It aims at optimizing the test strategy at a holistic level and grabbing insights into the broader picture of software testing. In contrast, test automation is about individual activities or precise tasks to be executed using test scripts and tools. Test orchestration is a broader approach to testing with the application of greater thought. It fosters transparency and offers flexibility in testing software applications or certain features of them during the development phases. In test orchestration, multiple automated test activities can be executed as part of the test pipeline.

Benefits of test orchestration

Test automation seeks to streamline automated testing activities and achieve optimal quality of testing. It offers a host of benefits to the DevOps teams, as discussed below:

Increased productivity: Test orchestration helps the testing of a software application across channels (devices, browsers, operating systems, and networks) and concerns (functionality, performance, and security, among others) through automation. This removes any arbitrariness in testing based on extraneous considerations and improves the productivity of the delivery team.

Better coverage: As test orchestration focuses on automation as an objective, the coverage of automation improves. With more tests being automated, the cost and timeline of delivery can be controlled or minimized.

Easy error remediation: Test orchestration leverages a continuous testing framework to automate testing arranged in a sequence of processes. This enables the identification and fixing of errors early when the complexity of the software application is low. In the absence of test orchestration, the possibility of identifying errors later in the SDLC would have increased, making their remediation a difficult exercise.

Optimized test process: Test orchestration allows the DevOps testing team to gain control over setting up the continuous test automation strategy and test schedules. And to achieve overall efficiency, test orchestration can leverage various tools such as project management tools, automated testing tools, and DevOps tools, among others. These tools, along with a continuous testing strategy, can transform the SDLC to achieve a flexible and optimized test process.

Faster builds: With a range of automated tests set up to be executed in a sequence, it becomes easier to create a test pipeline. This hastened the execution of tests, leading to faster builds.

Shortened feedback loops: In test orchestration, whenever a certain error or concern is flagged, say security, it is only the security experts in the testing team who would be involved in verifying the results. Thus, the direct remediation of errors by the concerned people due to a shortened feedback loop reduces the test timelines significantly.

Minimize the risk of failure: Test orchestration is put into motion based on the risk assessment of the software application. It allows the creation of dedicated pipelines to address risks related to scalability, performance, security, availability, and robustness, among others.

Conclusion

Test orchestration is undoubtedly the future of QA as it allows QA experts to achieve speed and quality of testing in a seamless manner. However, any successful implementation of test orchestration would need the QA team to possess the all-encompassing tools to deliver continuous testing in DevOps.

 Resource

James Daniel is a software Tech enthusiastic & works at Cigniti Technologies. I'm having a great understanding of today's software testing quality that yields strong results and always happy to create valuable content & share thoughts.

Article Source: medium.com

[webomates]

Intelligent Automation

Artificial Intelligence is a technique that enables a computer system to exhibit cognitive abilities and emulate human behavior based on pattern recognition, analysis, and learning derived from available data with the aid of predetermined rules and algorithms.

Machine learning and deep learning are two terms that are often used every time Artificial intelligence is discussed. People tend to use these interchangeably, however, there is a fundamental difference between them.

Understanding the fundamental difference between AI, ML, and DL

Artificial intelligence is the superset of machine learning and deep learning.

Machine learning is a subset of AI which aids computer systems in learning and decision making without explicit human intervention. It works on pattern recognition technology and works with predefined algorithms to understand, learn, process, infer and predict, based on past data and new information. Its prime focus is to aid in decision-making. AI improves as ML improves.

Deep Learning is a subset of machine learning, also called scalable machine learning. It helps machine learning algorithms by extracting zeta bytes of unstructured and unprocessed data from data sets.

What makes intelligent automation important in software testing

Test automation promised to revolutionize the world of testing when it was first perceived and implemented. It delivered on that promise by improving overall testing speed and results. However, as technologies and processes further evolved, there was a need for improving the testing process too.

If you want to understand the journey of the testing process from manual to AI era, then read our blog “Evolution of software testing”.

Automation eased the testing load, but it could not “think”. For instance, test automation can execute thousands of test cases and provide test results, but human intervention is needed when it comes to deciding which tests to run. Adding the dimension of intelligence can add analysis and decision-making capability to test automation.

Intelligent automation works on data like test results, testing metrics, test coverage analysis, etc., which can be extracted and utilized by AI / ML algorithms to identify and implement an improved test strategy for efficient testing.

As per the Gartner study, “By 2022, 40% of application development (AD) projects will use AI-enabled test set optimizers that build, maintain, run and optimize test assets”

Let us explore further how intelligently automating the testing process helps in improving overall QA operations.

Higher level of test reliability with improved accuracy In the era of DevOps with frequent and shorter development cycles, continuous testing is conducted for every minor/major change or a new feature.  While test automation has helped a lot in reducing the testing burden, adding AI to automation can enhance the overall testing process, since it keeps evolving based on new information and analysis of past data. It also aids the teams in identifying the tests for better test coverage. With intelligent automation tools doing a large portion of recurring tedious tasks, the developers and testers can focus on other aspects like exploratory testing and finding better automation solutions.

Improved risk profiling and mitigation with enhanced test result analysis Intelligent automation renders the ability of risk profiling to testing. Intelligent automation and analytics help the testing and development teams to have a better insight into the impact of code changes and risks associated with those changes. Appropriate actions can be taken based on these insights and issues can be intercepted much earlier

Deeper insights in test results and predictive analysis Test reports and analysis are critical processes of software testing. It helps the teams in understanding the loopholes in their current test strategy and consequently aids them to define better strategies for the next test cycle. AI-infused tools can analyze and understand the test results, spot the flaws and suggest the workarounds. These tools constantly learn and update their knowledge base with every test cycle, based on test result analysis and apply that knowledge to improve software testing by detecting even minor changes and predicting the test outcome. Improved defect traceability and prediction is a game-changer when it comes to optimizing the test strategies.

Boosts efficiency by transforming DevOps with benefits of AI Ops and QA Ops To match pace with dynamic software testing demands, DevOps has to be augmented with the power of artificial intelligence. QA Ops have gained importance in the past few years and enabling it further by using intelligent automation will ensure faster time to market with better quality.

Faster delivery with improved results Intelligent automation plays an important role in accelerating releases since it optimizes the whole testing process based on a comprehensive analysis of previous test results. Continuous testing for frequent changes can be time-consuming, but AI/ML expedites the whole process by identifying the right set of tests to be executed, thus saving a significant amount of time and resources.

Maximizing the benefits of test automation using Webomates Intelligent Automation solutions

Webomates provides intelligent automation solutions with intelligent analytics. It leverages the power of data processing, analysis, reasoning, and machine learning to provide an end-to-end testing solution for your business.

Self-healing test cases Agile development leads to frequent application updates. A good testing tool should be able to do the following:

Trace the changes to user stories/epics/requirements and update the tests accordingly.

Keep track of these changes while testing to ensure that nothing is broken.

In case of any issues, the involved teams should be notified on a priority basis and appropriate action should be taken.

Update the tests regularly based on the changes due to defect rectifications.

Webomates applies AI and ML algorithms to its self-healing test automation framework to dynamically understand the changes made to the application and modifies the testing scope accordingly. Webomates’ AI codeless engine effortlessly modifies (heal) the test cases, scripts and re-executes them within the same test cycle. Healed test suites lead to faster testing and development, thus speeding up the entire release process.

Defect reporting, triaging and tracing As stated in the previous section, defect tracking and tracing its source is an important analysis activity. It requires resources, time, and effort to conduct this exercise. Artificial Intelligence can help here by understanding and learning from software behavior. Webomates CQ provides a detailed test report with triaged defects. The QA and development team can access these reports, thereby enabling them to intervene on time and take appropriate action. Webomates’ Intelligent Analytics improvises your testing process by providing a continuous feedback loop of defects to requirements. Our ingenious AI Test Package Analyzer identifies all the test cases which are impacted due to a defect and traces them to impacted user stories/epics/requirements to identify the exact origin of the defect. This aids in understanding the root cause of the issue. The results of exploratory testing are analyzed by our test package analyzer. In case a module gets a high number of defects during exploratory testing, then it needs to be re-examined and more test cases need to be generated to cover all the possibilities.

Defect prediction based on test results With defect rectification and tracing sorted, imagine if the testing tool can predict potential issues and suggest corrective actions. That is exactly what Webomates’ AI Defect Predictor and Creator does. AI Defect Predictor helps in overcoming the challenges posed by false failures in automation. Consider an example, for 300 automated test cases with a failure rate of roughly 40%, the usual triaging time to identify false failures is around 12 hours. Using our tool, this time is reduced to just 3-4 hours. It not only differentiates true failures from false failures but also helps in creating a defect using the AI engine for True Failures.

Delivering value for money There are multiple options for automated testing available in the market. Many service providers offer AI as a part of their testing package. It is important to make the right choice from a business, financial and technical perspective. Webomates CQ is a financially and technically suitable option with the ability to scale up or down as per the customer requirement. We have a capable team of analysts and engineers to aid you along with the power of intelligent automation.

Intelligent automation (IA) is a technique to automate predefined repetitive testing tasks, using various test automation tools and testing scripts.

If this has piqued your interest and you want to know more, then please click here and schedule a demo. Partner with us and reach out at info@webomates.com.

If you liked this blog, then please like/follow us Webomates or Aseem.

[vikram739]

How to Scale DevOps to the Enterprise Level

Intense competition has driven enterprises across sectors to invest in DevOps and manage their digital systems. It helps to enhance developer velocity and enable digital transformation on a wider scale. At the same time, IT departments are embracing DevOps transformation to meet the evolving requirements of the organization and its customers in a rapidly changing digital economy. With the DevOps approach, large enterprises are able to develop software applications more rapidly than previously thought possible and keep upgrading such software applications on a continuous basis. The impact of the DevOps methodology is so apparent that enterprises having tightly coupled architectures have realized they have to either embrace it fully or be left behind. Thus, after realizing the benefits of adopting the DevOps approach, enterprises want to scale its adoption by cutting across the barriers of teams, applications, toolsets, workflows, processes, pipelines, and release cycles – both on cloud and legacy systems.

However, herein lies the catch: processes and tools that work flawlessly for use cases on a smaller scale may experience a hiccup when expanded to cover large teams and processes. This is due to the fact that the legacy architectures in such enterprises do not always allow seamless coordination among teams comprising hundreds of people and processes. What they need is an enterprise DevOps approach that addresses the unique challenges and lets them reach an agreement on the way to scale up DevOps. The DevOps methodology helps in increasing the impact and value of the digital infrastructure by combining legacy and new technologies. It allows enterprises to navigate change, derive time-to-value benefits, and lower risks. Thus, DevOps implementation should be about scaling DevOps principles and practices with quality, security, and velocity being embedded at every step of the way.  

How to scale DevOps to the enterprise level

The positives of DevOps in terms of establishing a continuous testing framework, integrations, and delivery, need to be scaled across organizations to achieve a host of objectives. It ought to be done in the following ways:  

Go for small wins and build on them: DevOps can fundamentally change the operational dynamics of an organization by breaking silos and driving everyone to work towards a common goal. This requires every stakeholder in the organization to buy into the approach or risk getting different results. The best DevOps approach would be to break down large complex projects into small, manageable entities and work. It will help the teams to learn quickly and act decisively. Teams working incrementally for smaller batches of work can identify and fix bottlenecks and achieve their objectives. Such small wins can help the team to build confidence in its ability to follow the DevOps methodology. The wins can give a boost to the project and enable the organization to apply DevOps across processes.

Management of end-to-end DevOps processes: To derive the optimum output through DevOps implementation, the platform should be manageable from a central location. To scale, there should be a single solution allowing the management of end-to-end workflow automation. With such centralized management, the organization can get clear visibility into the SDLC and other software assets. The job includes managing binaries, CI/CD pipelines, container images, risk and compliance, and last-mile deployments. Presently, several CI/CD tools allow the management of either workflows or outcomes, and not necessarily both. The DevOps platform should be able to automate and administer all processes, point tools, environments, and package types, and provide support for all technology artefacts and stacks. This allows the organization to plug into the toolset and/or legacy scripts and manage them from a single DevOps platform to achieve the following outcomes:

Consistency and traceability of the entire range of artefacts throughout the SDLC as they pass through the “development to production” pipeline. This gives a single source of validation for the entire DevOps processes and continuous testing framework, improves code quality, governance, and security, and speeds up software delivery.

Create a central repository for all binaries, DevOps testing, environments, container images, point tools, and others.

Manage risk and compliance across tools, processes, and repositories, including the ones from third parties, from code to production.

Get full visibility across the SDLC and the organization by breaking down silos or snowflake configurations. With the unified experience of working through a single platform, the organization can manage dependency downloads, deployments, pipelines, repositories, builds, and releases.

Bring every stakeholder on the same page: Most organizations have well-entrenched silos that can derail any initiative or innovation. So, even if the enterprise DevOps approach is successful in removing the silos or barriers, there is a risk of their re-emergence. This calls for motivating every stakeholder to buy into the silo-less DevOps-based ecosystem. And to do so would entail defining the desired behaviours and actions that every stakeholder is expected to perform and designing processes to reinforce the same. For instance, there may be a chance of conflicts arising if the ops team is measured only in terms of its ability to mitigate risks while the development team is measured in terms of its ability to deliver change. So, by applying inter or intra team collaboration metrics into the team and individual performance reviews, the organization can establish a common goal for everyone to work for.

Take the end-user into consideration: The ultimate objective of DevOps implementation is to deliver the best experience to the end-user. And, in the process of doing so, processes, systems, and work culture are changed and transformed. It is possible that in creating and enabling DevOps continuous integration, the sight of the end-user is lost. This should be actively guarded against by involving users in every step. Teams should take time to assess and understand the challenges, issues, constraints, and priorities of users. The knowledge so gained can be leveraged to define the KPIs or performance metrics and develop a continuous feedback mechanism.

Conclusion

Taking a DevOps approach across the organization is a long-term strategy and its success depends on the participation of every stakeholder. It is an evolving process or model that needs to be persisted with by experimenting with new ideas, capabilities, processes, and tools. Further, it is only when DevOps continuous integration is fully achieved across the organizational pipeline that enterprises can derive the best value.

Resource

James Daniel is a software Tech enthusiastic & works at Cigniti Technologies. I'm having a great understanding of today's software testing quality that yields strong results and always happy to create valuable content & share thoughts.

Article Source: medium.com