Be honest. Describe the state of your test cases.

“There’s some dead wood in there.”

“Hmmm…. Someone really needs to clean them up.”

“A little outdated.”

For those reading this in the northern hemisphere, spring time is here. Whether or not it is spring where you are, everyone could use a good spring clean in their lives every now and then, whether at home, or at work. Your test case repository is no different. Years can pass before an enterprise’s entire set of test cases get properly scrutinized. It is not such a big leap to compare test cases with, say, the cupboard where you’ve been putting things away for years. With both, you know it is something you need to properly sort through, but you are worried what you might find and how long it might take.

If this reminds you of your test case repository, test case optimization is something you should definitely seriously consider. Test case optimization is offered as part of ASTUTE, Apexon’s new AI-powered software testing services suite for faster digital transformation.

Test case optimization: an easy win for faster application delivery

Unlike spring cleaning your home or office, which involves manually sorting through mess, the first thing to note about test case optimization is that it is an AI-powered process, mainly driven by bots. In Apexon’s case, it is driven by TOBOT, our Test Optimization BOT combined with expert professional services. Test case optimization componentizes test cases for reusability, removes duplicates, deploys combinatorial analysis and optimizes the whole lot for ongoing maintenance.

The process is low-impact in terms of team time and TOBOT can finish the job in a matter of weeks, as opposed to months. The best thing about having a neat, shiny, ship-shape test case repository? Why, the quality and efficiency improvements, of course. The process ensures 100% coverage and improves optimization by 30-40% through semantic comparison and analysis. For enterprises, this means getting digital initiatives working faster in three main ways.

Improve quality – enables enterprises to identify optimal test case coverage and go ahead and enhance their coverage quickly and easily.

Operate efficiently – removes duplicates and organizes test cases for re-use, significantly freeing up in-house resources.

Ready for automation – primes test cases for faster, easier automation.

Improved optimization and faster release cycles: customer spotlight

Apexon worked with one of the largest financial institutions in the U.S. to remove all ‘dead’ test cases from a repository of over 150,000 while reducing future automation efforts by 30%.

We provided optimal test coverage through natural language processing, canonical models and semantic analysis. As a result, test case duplication was reduced by 12% and we identified 35% reusable steps across the repository. We then extracted the reusable test data and increased automation readiness of manual test cases.

Test optimization had a big impact for this client, and took less than 4- weeks to implement.

1. Coverage – Increased coverage using Model Based Testing approach while reducing redundant test cases by 12%

2. Reduced automation effort – Reusable test data sets and test steps resulted in faster automation implementation and reduced maintenance effort

3. Efficiency and go-to-market speed – reduced overall optimization efforts by over 30 percent, ensuring faster, more efficient automation and resulting in faster app updates for customers.

Talk to us today for a free assessment to see how AI-powered test optimization can help your organization.

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