Generative AI is a godsend for software quality teams and their executives. Indeed, Generative AI has changed software quality for the better in five substantive ways:
- Labor is no longer a constraint.
- 100% Application Coverage is the new normal.
- Test Scripts are now disposable.
- Regression Testing is now automatic.
- Functional, Performance, Load and Security Testing are now unified.
Collectively, these five changes usher in a new golden era of software quality. And, they elevate the quality function from mere testing to true Quality Engineering. Let’s drill down into each change.
Labor is no longer a constraint with Generative AI
Generative AI removes labor as a constraint in achieving outstanding software quality. This is obviously a game changer, even as it is a massive pain reliever for CTOs and VPs of Software and Quality, and for quality engineers. Why? Software quality teams tend to be chronically short of labor. Hence, from manual testers, to quality engineers, and to VPs, corners tend to get cut and uncomfortable compromises made. No more.
Generative AI allows quality engineers to manage the process of QA rather than perform the process of QA. They can then use their limited bandwidth to perform root cause analysis, trend analysis and other higher order tasks.
The result is that quality is dramatically enhanced, including by having existing staff perform quality engineering rather than getting bogged down with test creation and maintenance.
100% Application Coverage is the new normal
Software quality is experienced from the outside in. Users experiencing defects aren’t mollified by the fact that much of an application’s code or requirements were tested. Their UX is all that matters to them, and to the businesses who depend on their patronage.
Hence, previous measures of software quality, such as Test Coverage and Requirements Coverage, have been superseded by the essential new measure known as Application Coverage. This is because Test Coverage and Requirements Coverage reflect what non-users think will provide the best compromise level of coverage. That compromise was required due to the historic expense of software testing.
Those non-users are the Product Managers, QA engineers and Dev Managers who have long been saddled with software quality tooling that requires laborious test creation and execution. That meant that it has always been impossible to test everything due to staffing limitations.
Conversely, Application Coverage takes an outside-in approach, essentially mimicking the user experience. Application Coverage measures the extent to which every unique page and state were reached and whether every possible action in the application was executed during testing. This measure tests all the possible ways a user can use the application. It would have been time-consuming in the past to write and maintain scripts to achieve high Application Coverage. Now, Generative AI script creation capabilities make comprehensive Application Coverage readily achievable.
Test Scripts are now disposable
Test Scripts have always been managed like precious artifacts. That made sense since they were laboriously crafted by scarce and specialized resources, not unlike the code they are designed to test. Hence, test scripts were kept under version control regimes and painstakingly updated to reflect new capabilities of the code they were to test.
That reality has now been blown away by generative AI. Generative AI’s ability to instantly create a comprehensive suite of test scripts has created a new reality. Test scripts are now disposable!
Indeed, it’s best to have the generative AI spin up a new suite of test scripts for each build and test run. Why not, since a generative AI test platform like AIQ makes the creation of a suite of scripts labor-free, and that suite of AI-generated scripts will be vastly more comprehensive than even unlimited staff could have created.
Add test scripts to the long line of artifacts whose price points have been driven to zero by digital technology. This new economic reality has changed the game for software QA.
Regression Testing is now automatic
We test software so users don’t experience bugs. Well, people are unpredictable in advance. Retrospectively, they’re very predictable. Past is prologue when it comes to regression testing, as it were. Thus, users can be predicted to use existing functionality in the future as they’ve used it in the past, especially when it’s been used at scale.
Thousands of users engaging in tens-of-thousands or millions of sessions are likely to pursue initially surprising activity paths, but only initially. Over time and often very quickly, virtually every user action will get explored, and logged.
This phenomena leads to user-centric testing and from it, automatic regression testing. This is done by creating regression tests from production logs, which are simply detailed records of user activity paths. The answers to every question about what users attempt to do with the system are in the logs, if only those logs can be exploited. That used to be virtually impossible since production logs are too voluminous and obscure to be exploited by human testers.
That’s where generative AI test-scripting comes in. This unique capability of Appvance IQ creates large portfolios of regression testing scripts that are 100% user centric, doing so by applying a cognitive script generator to the task, and using production logs as a big-data source of learning.
The result is central to a new kind of regression testing. Automatic Regression Testing is distinctly different from previous forms of regression testing in many ways, one of which is that it drives 100% test coverage of everything users try to do with the system. The more users, the more scale, the better, since the resulting regression test portfolios become that much more pervasive.
Automatic Regression Testing, like all generative AI driven testing, is also virtually labor free. The AIQ generative AI platform automatically creates the tests and recreates them on an ongoing basis, and then automatically runs the tests. This changes the game for regression testing, making it a standard and essentially zero-cost capability of contemporary software QA.
Functional, Performance, Load and Security Testing are now unified
Once upon a time, functional testing was performed by a functional testing tool, driven by functional test plans. Load testing was performed by a load testing tool, driven by load test plans. Security testing was performed by a security testing tool, driven by security test plans.
Those days are gone, at least for companies that use a unified test platform that is driven by Generative AI. Appvance IQ is just such a platform. AIQ unifies all forms of software testing into one platform. There are two elements to this unification.
First, test plans in AIQ drive all forms of testing: functional, load, performance and security. So there are no more “functional test plans” or “performance test plans”. Even if those test plans weren’t auto-generated via generative AI, this inherent repurposing of test plans for the various forms of testing eliminates the silos of test plans that previously existed and that still do for users of non-unified test platforms.
Universal test plans are then fed into AIQ’s test execution machine, which includes a functional test execution engine, performance test execution engine, security test execution engine and load test execution engine. Hence, all forms of test execution are now driven by generative AI.