10 Ways To Accelerate DevOps With AI

Software programming has evolved over the decades and consequently, testing, which is an integral part of software development, has also undergone a series of changes.

It all started with ad-hoc testing by developers and testers, traversing through the era of manual and automation testing, to continuous testing which is integral for DevOps. The introduction of artificial intelligence in software testing opened doors for faster and reliable testing solutions which helped in delivering high-quality end products.

Read a detailed account of this journey on our blog “Evolution of software testing”. The focus of the current article is on how artificial intelligence is changing the face of software testing by accelerating DevOps.

10 Ways to Accelerate DevOps with AI

DevOps helps organizations in keeping pace with the market dynamics by building, testing, and releasing the software faster. Multiple build-test-release cycles generate massive data which is then monitored and analyzed for improvising the next cycle. It is difficult to sift through such a huge amount of data manually after every cycle, especially if the time is of the essence.  DevOps augmented with AI is a perfect solution to improvise and speed up CI/CD/CT pipeline in a reliable manner. This is just one example where AI aids in increasing DevOps efficiency. There are more ways in which AI can help in accelerating DevOps, covered in the entailing section.

1. Codeless Testing :

Codeless testing frees up the testing team from the tedious task of writing test scripts and makes way for them to focus on other important tasks. It not only increases efficiency by saving time and resources but also expands the scope of test automation.

We have covered the top benefits of codeless testing in our blog. Do take out some to read it by clicking here “Top 5 benefits of codeless testing”.

2. Better Test Coverage:

AI algorithms are equipped with the capabilities of diving deep into the massive volume of data at its disposal and identify multiple test scenarios that can expand the test coverage, which is a herculean task if done manually. It also uses its analytical capabilities to prioritize the tests based on predictive analysis.

3. Improved Test Accuracy and Reliability:

Multiple release cycles in a short time involve executing a large number of test cases. While automation has taken this task from the tester’s hands, AI has gone a step further by improvising and evolving the testing with every testing cycle. This makes testing more reliable, efficient, and accurate.

4. Self-Healing Test Automation:

Agile development leads to frequent changes in code due to defect rectifications and new/updated requirements. This consequently leads to changes in test cases. AI algorithms integrated with the test automation framework can understand these changes and effortlessly self-heal the tests and re-execute them within the same cycle, thus speeding up the entire testing process.

5. Effective Defect Management :

Defect management is an important aspect of software testing. Its scope is not limited to development and testing but also extends to defects encountered by the end-users when they use the system. Using artificial intelligence to troubleshoot and manage these defects significantly improves QAOps and DevOps. Read our blog “Intelligent analytics with AI testing” for further details.

6. Improved and Full Proof Defect Tracing:

Defect identification, triaging, and tracing the defect to its origin is a key task of defect management. Defects can be introduced at any stage of development, right from conceptualization to any point till realization. Debugging and tracing is a time-consuming exercise. AI-based tools can perform this tedious task effortlessly.

7. Integrating Predictive Analysis with Test Results:

Testing result analysis enhances the software development process by identifying and aiding the teams in managing the flaws in their test strategies. AI-based testing 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. It not only expedites the whole process but also makes it more intuitive and accurate.

Based on data collated from past test results and current test strategies, AI-based testing tools can mitigate risks by executing test cases for high-risk modules. This predictive analysis can optimize the testing process by a major degree. We have covered the test optimization aspect in our blog “How intelligent automation optimizes your testing”. Do spend some time reading it for better insights.

8. Enhanced Test Failure Analysis:

Test failures are part and parcel of DevOps. It is important to manage these failures by analyzing them to understand the root cause and how it can be addressed to prevent such scenarios in the future. The AI model not only segregates the defects based on their priority but also analyzes the defect to understand the cause and notifies the teams accordingly. AI-based algorithms can understand the patterns and predict potential issues. These predictions aid the development teams to fix the issues before it is too late.

Do you want to know more about Test failure analysis with AI? If yes, then read our article on the subject by clicking here. 

9. Better Collaboration and Communication:

Coordination, collaboration, and communication are important cogwheels of the DevOps cycle. The test results and insights need to be shared with all the stakeholders, technical and business. It aids them in improving their business and technical strategies for accelerated development and delivery. AI helps the teams in staying updated with every aspect of testing. The test results are analyzed and reported to the stakeholders instantly. It helps in early intervention by the concerned team/person to address the issue.

10. Continuous Feedback Loop :

DevOps can be successful if the feedback generated at every stage of development and testing is continuously shared with all the stakeholders in an easily comprehensible format. Any delayed or skipped feedback can have an adverse effect on the software development process. AI-based tools can issue timely alerts to the teams about any problems/defects and aid them in prioritizing and managing them effectively. This comes in handy, especially when the whole CI/CD/CT pipeline is churning out huge data with every cycle which can overwhelm the testers if done manually.

Accelerating DevOps with Webomates

AI enhances the benefits of test automation and further accelerates DevOps’ performance and productivity. Using artificial intelligence engines, QA teams can trigger unattended testing cycles. It is the AI engine’s job to execute the tests, identify and report the right defects and suggest a course of action.

Webomates’ AI engine checks all the above-mentioned points and helps their customers in developing a holistic DevOps strategy by seamlessly integrating with their CI/CD pipeline.

Webomates provides AI testing solutions with intelligent analytics which aids in accelerating development cycles ensuring high-quality results. The following figure highlights the Webomates’ AI testing capabilities which act as prime accelerants for the DevOps cycle.

Our codeless engine has an AI discovery and generation tool that generates test scripts that are compatible with multiple automation frameworks. AI Modeler engine aids in generating and automating the right test cases.

Webomates’ AI codeless engine effortlessly modifies (heals) 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. Our self-healing testing process is covered in detail in our blog “Self-healing- Automate the automation”.

Our ingenious AI defect predictor can identify false positives with 99% accuracy. It can predict defects much earlier in the test cycle, thus saving thousands of man-hours spent in triaging. 

AI test package analyzer provides a continuous feedback loop that ensures that requirement changes are traced at every level and updated accordingly.

Defects discovered during testing are triaged and detailed feedback is provided to the stakeholders via real-time alerts.

A comprehensive testing report and informational video are generated for all the tests. It aids the CQ users in having a holistic view of the testing results of their application. The testing and development team can access these reports at any point in time for analysis and quick resolutions. This adds significant value to the QA Ops process.

Webomates CQ is a financially and technically suitable option with the ability to scale up or down as per the customer requirement.

We can conduct testing as per the scope of the build saving thousands of man-hours.

• Full regression test which takes 24 hours
• Overnight regression at the modular level which takes 8 hours
• CI/CD that may take 15 minutes to 1 hour.

Webomates has a competitive edge over many others with its patented intelligent automation and analytics tool, which provides value for money to its customers. Additionally, we provide service level guarantees to all our customers.

At Webomates, we continuously work to evolve our platform and processes, to provide guaranteed execution, which takes testing experience to an entirely different level, thus ensuring a higher degree of customer satisfaction.

Partner with us for accelerating your DevOps. Click here to schedule a demo, or reach out to us at info@webomates.com .

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Tags: Agile, AI Testing, Artificial Intelligence, DevOps, Intelligent automation, Test Automation