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Defect predictors, static bug detectors and humans inspecting the code canlocate the parts of the program that are buggy before they are discoveredthrough testing. Automated test generators such as search-based softwaretesting (SBST) techniques can use this information to direct their search fortest cases to likely buggy code, thus speeding up the process of detectingexisting bugs. However, often the predictions given by these tools or humansare imprecise, which can misguide the SBST technique and may deteriorate itsperformance. In this paper, we study the impact of imprecision in defectprediction on the bug detection effectiveness of SBST.  Our study finds that the recall of the defect predictor, i.e., theprobability of correctly identifying buggy code, has a significant impact onbug detection effectiveness of SBST with a large effect size. On the otherhand, the effect of precision, a measure for false alarms, is not of meaningfulpractical significance as indicated by a very small effect size. In particular,the SBST technique finds 7.5 less bugs on average (out of 420 bugs) for every5% decrements of the recall.  In the context of combining defect prediction and SBST, our recommendationfor practice is to increase the recall of defect predictors at the expense ofprecision, while maintaining a precision of at least 75%. To account for theimprecision of defect predictors, in particular low recall values, SBSTtechniques should be designed to search for test cases that also cover thepredicted non-buggy parts of the program, while prioritising the parts thathave been predicted as buggy.

How good does a Defect Predictor need to be to guide Search-Based Software Testing?