Why Implementing AI Means Automating More

Test Automation: Why More is Better

This isn’t another blog promoting test automation or praising its advantages over manual testing. It highlights why increasing automation in testing is a smart decision and highly beneficial to the software testing life cycle (STLC).

Autify is assertive about its vision for customer platform implementation, outlining three phases in the proposed roadmap. Let’s delve into each phase.

Increase Automation Coverage

Test coverage is the measure determining the extent of software code tested and covered by our tests. While automation coverage indicates the number of tests executed with some form of automation.

In this phase, Autify’s goal is to increase the percentage of automated test executions in its customers’ projects.

Increase Overall Coverage

Autify promotes the adoption of the Shift Left methodology, urging customers to test early in the SDLC rather than just before deployment. This minimizes resource concentration during the test phase, aligning the entire SDLC with testing from analysis to deployment.

Testing in the planning phase shifts the team’s mindset towards testing development work before coding. This iterative process alternates between testing and other SDLC phases and lays the groundwork for expanding testing.

Achieving complete test coverage involves utilizing a mix of manual and automated scenarios, with a specific emphasis on the latter. It’s crucial to note that 100% coverage doesn’t guarantee a bug-free application, emphasizing the importance of making testing the driving force behind the process.

Increasing automation coverage enables overall coverage improvement, and embracing the Shift Left approach empowers the team to optimize automated test execution to the fullest extent.

Eliminate Test Phase

Automating tests at the initial stage of development addresses inefficiencies and potential bugs at the analysis, planning, strategy, and code levels, preventing their accumulation at the test execution stage.

Smarter Approach to Shift Left

Shift left testing is a challenge for both manual and automated testing, but automation significantly improves the process by enabling more test executions.

Various coded or full-code solutions exist, each with pros and cons. However, implementing coded tools demands time, expertise, and intellectual resources.

Tibor Uittenbogaard, former digital marketing consultant from One Shoe once stated: “Tests should evolve to provide more and more quality and quality assurance along the way. For every new insight, bug or feature, the developers should ask themselves if an automated test can prevent future bugs, and they should assess if a test shall have a level of added value that is equal to-or greater than the cost –time– of implementing the test.”.

Following this line of thought, we can conclude that anything that adds simplicity to the process is the smarter choice.

Another layer of complexity must be removed if automation script writing, manual script maintenance, or handling locators on an application UI after codebase changes impede a dynamic shift left practice. No-code and low-code tools offer a smarter choice to facilitate this issue.

Productivity Exponentiated

Shifting left is a means to save resources like time and money, and this practice can be further improved and simplified through automation. AI solutions also present a logical response to these problems.

Less is More

Achieving more with less is a clear goal. What if removing factors from the productivity equation is the key to achieving even more?

Reducing complexity in setting up a test environment yields substantial gains in both time and resources required to initiate the process. This simplicity impacts the creation of more robust test scenarios capable of covering an extensive array of use cases.

Exploring test plans across various browsers, devices, and platforms reveals a clear connection between simplicity and improved test coverage. Streamlining this process results in a broader and more comprehensive testing spectrum.

The impact of the above also extends to generating test statistics reports that provide insights for smarter, richer, and more controlled testing practices.

Lastly, adopting a zero-complexity approach to test maintenance eliminates redundancies, significantly reduces manual intervention, and virtually eradicates headaches linked to testing procedure upkeep.

Can Autify Deliver That?

Our customers speak for us:

RecoChoku, a Japanese music streaming platform, reported a 75% reduction in test execution times achieved by their QA team through Autify’s parallel test execution. This improvement not only enhanced efficiency and project clarity but also eliminated the need to outsource staff, particularly during the execution of regression tests for primary user flows.

With straightforward usability and faster execution times, Rechoku no longer handles UI locators individually during scenario creation. Autify’s flexibility and user-friendly interface extend to hiring processes, enabling seamless integration of non-programmers into the QA team.

GA Technologies, a Japanese real estate technology company, acknowledges reducing test environment construction time and costs to zero, and significantly reduced test maintenance time using Autify. Leveraging a flexible execution environment also contributed to higher conversion rates.

Conclusion

In conclusion, coded automation solutions enhance the testing process by eliminating manual hurdles and improving production. The impact of AI tools multiplies these benefits, leading to wider and deeper test coverage. This not only fosters better human participation in the tech industry but also ensures a heightened level of quality in the end products.

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