AI is writing more code. Often, more tests as well.
But that doesn’t mean the effort just disappears. He moves on to the review, debugging, and the question of whether the tests are effective.
The pipeline is green. The coverage looks good. And yet, with some modules, the question remains: Do the tests identify errors that are relevant to the subject matter?
This is exactly where mutation testing can be useful. The process makes targeted changes to small parts of the code and checks whether the existing tests detect the changes. If they remain green even though there have been some technical changes, the safeguards may be less robust than expected.
Mutation testing is neither a new large-scale project nor a blanket quality gate. Es erzeugt Laufzeit und Bewertungsaufwand.
It results in runtime and evaluation overhead.Deshalb startet man klein: ein kritisches Modul, eine bestehende Testbasis, ein klar begrenzter Zeitraum, eine fachliche Auswertung.
The value lies not in running the tool, but in the analysis: Which tests are effective? Where exactly does coverage come from? Which gaps are significant enough to warrant addressing?
Modules with a green pipeline but limited confidence in the tests are often a good place to start.
AVABIS provides expert analysis of the results to support such studies.
If you’re interested or have any specific questions, please feel free to contact us.
