RT Journal Article
T1 HybridCISave: A Combined Build and Test Selection Approach in Continuous Integration.
A1 Jin, Xianhao
A1 Servant-Cortés, Francisco Javier
A2 Pezze, Mauro
K1 Ingeniería del software
AB Continuous integration (CI) is a popular practice in modern software engineering. Unfortunately, it is also a high-cost practice — Google and Mozilla estimate their CI systems in millions of dollars. To reduce the computational cost in CI, researchers developed approaches to selectively execute builds or tests that are likely to fail (and skip those likely to pass). In this paper, we present a novel hybrid technique (HybridCISave) to improve on the limitations of existing techniques: to provide higher cost savings and higher safety. To provide higher cost savings, HybridCISave combines techniques to predict and skip executions of both full builds that are predicted to pass and partial ones (only the tests in them predicted to pass). To provide higher safety, HybridCISave combines the predictions of multiple techniques to obtain stronger certainty before it decides to skip a build or test. We evaluated HybridCISave by comparing its effectiveness with the existing build selection techniques over 100 projects, and found that it provided higher cost savings at the highest safety. We also evaluated each design decision in HybridCISave and found that skipping both full and partial builds increased its cost savings and that combining multiple test selection techniques made it safer.
PB Association for Computing Machinery (ACM)
YR 2023
FD 2023-05-26
LK https://hdl.handle.net/10630/34857
UL https://hdl.handle.net/10630/34857
LA eng
NO Xianhao Jin and Francisco Servant. 2023. HybridCISave: A Combined Build and Test Selection Approach in Continuous Integration. ACM Trans. Softw. Eng. Methodol. 32, 4, Article 93 (July 2023), 39 pages. https://doi.org/10.1145/3576038
NO This material is based upon work supported by the National Science Foundation under award CCF-2046403, and by Universidad Rey Juan Carlos under an International Distinguished Researcher award C01INVESDIST.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026