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Test Case Prioritization (TCP) is an increasingly important regressiontesting technique for reordering test cases according to a pre-defined goal,particularly as agile practices gain adoption. To better understand thesetechniques, we perform the first extensive study aimed at empiricallyevaluating four static TCP techniques, comparing them with state-of-researchdynamic TCP techniques across several quality metrics. This study was performedon 58 real-word Java programs encompassing 714 KLoC and results in severalnotable observations. First, our results across two effectiveness metrics (theAverage Percentage of Faults Detected APFD and the cost cognizant APFDc)illustrate that at test-class granularity, these metrics tend to correlate, butthis correlation does not hold at test-method granularity. Second, our analysisshows that static techniques can be surprisingly effective, particularly whenmeasured by APFDc. Third, we found that TCP techniques tend to perform betteron larger programs, but that program size does not affect comparativeperformance measures between techniques. Fourth, software evolution does notsignificantly impact comparative performance results between TCP techniques.Fifth, neither the number nor type of mutants utilized dramatically impactmeasures of TCP effectiveness under typical experimental settings. Finally, oursimilarity analysis illustrates that highly prioritized test cases tend touncover dissimilar faults.

How Do Static and Dynamic Test Case Prioritization Techniques Perform on Modern Software Systems? An Extensive Study on GitHub Projects