Abstract–As software evolves, problematic changes can significantly degrade software performance, i.e., introducing performance regression. Performance regression testing is an effective way to reveal such issues in early stages. Yet because of its high overhead, this activity is usually performed infrequently. Consequently, when performance regression issue is spotted at a certain point, multiple commits might have been merged since last testing. Developers have to spend extra time and efforts narrowing down which commit caused the problem. Existing efforts try to improve performance regression testing efficiency through test case reduction or prioritization.
In this paper, we propose a new lightweight and white-box approach, performance risk analysis (PRA), to improve performance regression testing efficiency via testing target prioritization. The analysis statically evaluates a given source code commit’s risk in introducing performance regression. Performance regression testing can leverage the analysis result to test commits with high risks first while delaying or skipping testing on low-risk commits.
To validate this idea’s feasibility, we conduct a study on 100 real-world performance regression issues from three widely used, open-source software. Guided by insights from the study, we design PRA and build a tool, PerfScope. Evaluation on the examined problematic commits shows our tool can successfully alarm 91% of them. Moreover, on 600 randomly picked new commits from six large-scale software, with our tool, developers just need to test only 14-22% of the 600 commits and will still be able to alert 87-95% of the commits with performance regression.