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

TL;DR

This extensive empirical study compares static and dynamic test case prioritization techniques on real-world GitHub Java projects, revealing insights into their effectiveness, correlations at different granularities, and impact of program size and evolution.

Contribution

It provides the first comprehensive empirical evaluation of static versus dynamic TCP techniques across multiple quality metrics on large, real-world software projects.

Findings

Static techniques can be surprisingly effective, especially with APFDc.

TCP performance improves with larger programs.

Program evolution and mutant types have limited impact on effectiveness.

Abstract

Test Case Prioritization (TCP) is an increasingly important regression testing technique for reordering test cases according to a pre-defined goal, particularly as agile practices gain adoption. To better understand these techniques, we perform the first extensive study aimed at empirically evaluating four static TCP techniques, comparing them with state-of-research dynamic TCP techniques across several quality metrics. This study was performed on 58 real-word Java programs encompassing 714 KLoC and results in several notable observations. First, our results across two effectiveness metrics (the Average Percentage of Faults Detected APFD and the cost cognizant APFDc) illustrate that at test-class granularity, these metrics tend to correlate, but this correlation does not hold at test-method granularity. Second, our analysis shows that static techniques can be surprisingly effective, particularly when measured by APFDc. Third, we found that TCP techniques tend to perform better on larger programs, but that program size does not affect comparative performance measures between techniques. Fourth, software evolution does not significantly impact comparative performance results between TCP techniques. Fifth, neither the number nor type of mutants utilized dramatically impact measures of TCP effectiveness under typical experimental settings. Finally, our similarity analysis illustrates that highly prioritized test cases tend to uncover dissimilar faults.