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 foolproof 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.Read More about : Api Testing

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