PerfRanker: Prioritization of Performance Regression Tests for Collection-Intensive Software
Regression performance testing is an important but time/resource-consuming phase during software development. Developers need to detect performance regressions as early as possible to reduce their negative impact and fixing cost. However, conducting regression performance testing frequently (e.g., after each commit) is prohibitively expensive. To address this issue, in this paper, we propose PerfRanker, the first approach to prioritizing test cases in performance regression testing for collection-intensive software, a common type of modern software heavily using collections. Our test prioritization is based on performance impact analysis that estimates the performance impact of a given code revision on a given test execution. Evaluation shows that our approach can cover top 3 test cases whose performance is most affected within top 30% to 37% prioritized test cases, in contrast to top 65% to 79% by 3 baseline techniques.
Mon 10 JulDisplayed time zone: Tijuana, Baja California change
10:30 - 12:10 | |||
10:30 25mTalk | One Test to Rule Them All Technical Papers Alex Groce Northern Arizona University, Josie Holmes Pennsylvania State University, USA, Kevin Kellar DOI | ||
10:55 25mTalk | Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration Technical Papers Helge Spieker Simula Research Laboratory, Norway, Arnaud Gotlieb Simula Research Laboratory, Norway, Dusica Marijan Simula, Morten Mossige University of Stavanger, Norway / ABB Robotics, Norway DOI | ||
11:20 25mTalk | PerfRanker: Prioritization of Performance Regression Tests for Collection-Intensive Software Technical Papers Shaikh Mostafa University of Texas at San Antonio, USA, Xiaoyin Wang University of Texas at San Antonio, USA, Tao Xie University of Illinois at Urbana-Champaign DOI | ||
11:45 25mTalk | Compiler-Assisted Test Acceleration on GPUs for Embedded Software Technical Papers Vanya Yaneva University of Edinburgh, UK, Ajitha Rajan University of Edinburgh, UK, Christophe Dubach University of Edinburgh DOI |