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Ensuring that software performance does not degrade after a code change isparamount. A solution is to regularly execute software microbenchmarks, aperformance testing technique similar to (functional) unit tests, which,however, often becomes infeasible due to extensive runtimes. To address thatchallenge, research has investigated regression testing techniques, such astest case prioritization (TCP), which reorder the execution within amicrobenchmark suite to detect larger performance changes sooner. Suchtechniques are either designed for unit tests and perform sub-par onmicrobenchmarks or require complex performance models, drastically reducingtheir potential application. In this paper, we empirically evaluate single- andmulti-objective search-based microbenchmark prioritization techniques tounderstand whether they are more effective and efficient than greedy,coverage-based techniques. For this, we devise three search objectives, i.e.,coverage to maximize, coverage overlap to minimize, and historical performancechange detection to maximize. We find that search algorithms (SAs) are onlycompetitive with but do not outperform the best greedy, coverage-basedbaselines. However, a simple greedy technique utilizing solely the performancechange history (without coverage information) is equally or more effective thanthe best coverage-based techniques while being considerably more efficient,with a runtime overhead of less than 1%. These results show that simple,non-coverage-based techniques are a better fit for microbenchmarks than complexcoverage-based techniques.

Evaluating Search-Based Software Microbenchmark Prioritization