AI-Driven Testing

Preface

Software testing is expensive. Testing requires both up-front and recurring investment in labor and assets to reduce the risk of shipping a product that does not meet customer expectations. Good testers and engineers with knowledge and experience applying software-testing best practices are hard to find. By the end of a project, you’ve spent 30%–50% of your engineering budget on software-testing activities,¹ including requirements and design testing, unit testing, user acceptance testing, performance testing, and security testing.

Not investing enough in testing can have an even worse impact on your organization’s bottom line. The cost of fixing defects increases exponentially with the time and stage at which they occur during the development cycle. Bugs found early during requirements elicitation are much cheaper to fix than those discovered while coding or, worse yet, after the software is released. Bugs that do escape to production directly impact customer satisfaction and can ultimately cost you your reputation and business.

The key to controlling quality cost-effectively is to find the right level of testing effort for your project based on the risks associated with the release. Ideally, you want to do just enough testing that you can remove the most harmful defects prior to the release. Of course, the more test coverage, the better, but if that coverage comes with too great an investment cost, you end up overtesting and not getting a good return on your investment ...