Artificial Intelligence (AI) In Agile And DevOps

Artificial Intelligence

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8 May 2022
7 Min read

Role Of Artificial Intelligence (AI) In Agile And DevOps

Our IT system’s flexibility, security, and resilience have never been better, thanks to fast-evolving business ecosystems, regulatory settings, and consumerization of IT demands.

Artificial intelligence (AI) has already transformed every area of business and operations and the underlying IT systems and development processes. While Agile and DevOps are already helping to streamline and speed the SDLC process, there are still difficulties to overcome in prevalent mindsets and skill shortages to reach hyperautomation and continuously use best-in-class engineering methods.

To develop models and find trends, artificial intelligence (AI) and machine learning (ML) can come to the rescue by gathering massive chunks of data generated by various software engineers, including CI/CD systems. These models may be used to identify anomalies, anticipate failures, and provide remediation, allowing us to take a giant leap forward in developing high-performance autonomous systems.

Let’s look at how AI may help at different levels of DevOps:

Consistent Planning

Stakeholders in the business want applications to deliver new capabilities and handle concerns quickly. Thanks to continuous planning, inputs are received in various structured and unstructured ways, such as product or service requests, issue tickets, customer feedback, surveys, and market analyses. These inputs are assessed regularly, then translated into user stories and added to the product backlog.

Natural language processing (NLP) can interpret unstructured inputs such as emails, voice messages, phone calls, and online comments. It aids in better capturing the user’s requirements and pain areas in conjunction with the right intent. These data can also be compiled and summarised to provide product owners and other business stakeholders insights, planning and prioritizing features and bug fixes for future releases.

Continuous Integration

This stage entails integrating code from diverse developers and making incremental regularly builds to reduce risk. In the event of faults or failures, a chatbot with Natural Language Generation (NLG) capacity can help trigger on-demand and deliver personalized alerts and messages. Furthermore, historical data from past code changes builds, and logs created can be evaluated to uncover patterns and identify hotspots for avoiding future mistakes. Other critical operations that can benefit from artificial intelligence (AI) include static code analysis and unit testing.

The code analysis findings can be supplied into a conversation engine once activated in the background and completed after a developer submits the code. It can use a text summarising engine translated to voice to describe the results, advising the developer to enhance the code quality before testing.

Continuous Testing

Beyond test execution and reporting, artificial intelligence (AI) can supplement less evident but crucial auxiliary operations in the quality assurance (QA) process. For example, test engineers can use an intelligent assistant to automatically classify faults and discover any duplication during the testing process. This can dramatically improve the defect triaging process, which is currently inefficient and time-consuming.

Logs from failed tests can be analyzed to find repeating trends, allowing models to be built and trained to anticipate failures in future test runs. NLP can be used to turn test cases into scripts that can be fed directly by popular automated testing frameworks like Selenium or Appium for systems in production where most test cases are already accessible. Comparative tests can be organized into clusters based on patterns deriving from semantic similarity and history of success or failure to reduce time and optimize regression testing.

Continuous Deployment

From the days when deployment jobs were manually initiated using handwritten scripts to today’s single-click multi-stage automated deployment, technology has played a critical role in automating software deployment. Despite this progress, many organizations continue to experience unsuccessful and sub-optimal deployments with repeated rollbacks, resulting in delayed launches and lost revenue. Artificial intelligence (AI) can help handle the complexity of installations while also lowering failure rates.

For example, ontologies representing an organization’s infra-assets, such as software, databases, and hardware, can be built for dev-test, staging, and production settings. A mix of subject matter expert knowledge, Configuration Management Databases (CMDBs), and network discovery tools can be used. System and application-specific logs generated during previous deployments can be saved, parsed, and evaluated with ontology elements to forecasting potential errors in future implementations. These failures can be compared to accurate deployment results to uncover new patterns from which preventive measures can be taken to make future deployments more predictable and dependable.

Feedback And Continuous Monitoring

Product owners, QA, and development teams can monitor production releases to see how the applications are working and being utilized. The applications, dependent systems, tools, and other network components generate massive amounts of data in alerts, issues, logs, events, and metrics. By employing supervised and unsupervised learning to create trained models, artificial intelligence (AI) can aid in the extraction of insights from this vast data set. These models can help detect unusual behavior that could lead to security flaws and failures.

Direct input on end-user concerns can also be gathered through other channels such as emails, text messages, and voice-based interactive chats. This feedback and usage patterns can be analyzed to improve sentiment and usability assessments while gaining a more profound knowledge of the customer’s experience with the product or service. Finally, the results of this analysis can be used as a vital input for perfective maintenance or the design of new user stories that will improve the user experience.

Conclusion

Today, digital technologies are altering firms in a variety of industries. DevOps plays a critical role in this transformation tale by guaranteeing that new-age technologies-based products and services are ready for consumption seamlessly and reliably. AI promises to take the DevOps movement to the next level by injecting intelligence based on best practices and minimizing human and system faults. This will not only shorten the time it takes to go from concept to deployment, but it will also allow us to achieve the seemingly impossible objective of creating flexible, self-learning, and responsive autonomous systems. To know more about artificial intelligence (AI), contact the ONPASSIVE team.

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