Today’s fast-moving software delivery environment emphasizes on bringing value to production, as quickly as possible. The popularization of DevOps and Cloud Computing has revolutionized the software delivery process, making it faster and affordable for business, to release their software continuously.
We know from DevOps methodologies, that better testing certainly improves overall outcomes and reduces post integration flaws. In my opinion, testing maturity is a key differentiator of best practices in DevOps delivery. Many organizations can automate their integrations, builds and delivery processes, but still have trouble with the subtleness of test orchestration and automation. I see test architects and testing teams playing an important role in offering their expertise for test design, test automation and test case development with DevOps. Whether the organization is using a test-driven development methodology, behavior-based test creation, and model-based testing, testing is a vital part of the overall DevOps process. This not only ensures verification code changes work and integrate well, but also, to ensure that changes do not rupture the product.
Automating and orchestrating your DevOps delivery pipeline:
“Automation” is frequently used in the context of automating a project’s deployment pipeline. Continuous Integration, Continuous Deployment, and Continuous Delivery are all facets of this overarching domain of DevOps, which leverages automation and tools to quickly build, test, deploy, re-test, and promote software changes through environments to eventually deploy in to production.
Before we get caught up in exuberance of this new domain, which is disrupting the QA market, let’s take a step back and assess how we can achieve our quality objectives in DevOps and Agile mode of delivery.
- Test Orchestration– Orchestration looks at the entire testing operation to figure out exactly where optimization can take place. In my opinion, orchestrating all the testing tools on a single platform can prove to be smart and resourceful in DevOps.
- Business Process Testing– To make our entire QA process aligned with our client’s business goals, I feel the need of having a framework which can predict the scope of testing, suggest critical test scenarios, automate the way to extract impacted areas and extract dependencies & related functionalities.
- Change Impact Analysis– If you ask me, change impact analysis is a complex activity, as it is very difficult to predict the impact of a change in software. An intelligent tool to help identify the impacted areas in the software, and map them with the test cases and the various screens, is need of the hour.
- Predictive Analytics– Most of the times, logs are a jumbled mess of millions of transactions, but they can serve as a “big data” source to understand user flows. An easy-to-miss bonus here is, with the knowledge of real user flows, the AI system can generate an outstanding set of tests that can represent what real users do, better. I believe this real-user representation aspect can turn out to be a breakthrough for the test community.
- Defect Analytics– Haven’t we not often observed that most of the time today, is consumed in defect reassignment and figuring out how to resolve them. If you ask me, AI-based predictors that learns specific patterns concerning defect-density from past projects and use this information to predict defect proneness for new projects, is something that can be of great help to make the defect triage process better.
- Smart Test Data Creation– In general, valid test input data (such as credentials) must be provided by the test team, or by a connected database. A system which can smartly make use of the right data in the right positions, is a must.
- Live Build Status and Hyper-collaboration– Provision of real-time and interactive dashboards for a unified view, can turn out to be a boon in DevOps delivery style, to monitor progress of deployment as it happens, rather than wait for manual confirmation of success or failure at the end.
- Code Coverage– Last but not the least, AI-based automated unit testing can considerably reduce dependency on developers & improvise unit test coverage. There is a need of tool that can take legacy code, run it through the special bot and suggest comprehensive unit tests. The tool should be able to know what’s happening in the code, it can analyze it and run it in different permutations and combination to ensure code coverage.
So, don’t you think, incorporating proper Quality Management is both logical and necessary, as DevOps continues to play an important role in Transformation Journey? Hence, a platform which can fail fast/fail early, automate everything, balance velocity with time and which is scalable enough, needs to be an inherent part of your QA solution. By evaluating the ways automated testing and test orchestration can be achieved E2E through a single platform, it is deemed to bring in benefits of predictive and highly automated processes, better traceability, providing efficient DevOps delivery.
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