History-driven Build Failure Fixing: How Far Are We?
Build systems are essential for modern software development and maintenance since they are widely used to transform source code artifacts into executable software. Previous work shows that build systems break frequently during software evolution. Therefore, automated build-fixing techniques are in huge demand. In this paper we target a mainstream build system, Gradle, which has become the most widely used build system for Java projects in the open-source community (e.g., GitHub). HireBuild, state-of-the-art build-fixing tool for Gradle, has been recently proposed to fix Gradle build failures via mining the history of prior fixes. Although HireBuild has been shown to be effective for fixing real-world Gradle build failures, it was evaluated on only a limited set of build failures, and largely depends on the quality/availability of historical fix information. To investigate the efficacy and limitations of the history-driven build fix, we first construct a new and large build failure dataset from Top-1000 GitHub projects. Then, we evaluate HireBuild on the extended dataset both quantitatively and qualitatively. Inspired by the findings of the study,we propose a simplistic newtechnique that generates potential patches via searching from the present project under test and external resources rather than the historical fix information. According to our experimental results, the simplistic approach based on present information successfully fixes 2X more reproducible build failures than the state-of-art HireBuild based on history fix information. Furthermore, our results also reveal various findings/guidelines for future advanced build failure fixing.
Wed 17 Jul Times are displayed in time zone: (GMT+08:00) Beijing, Chongqing, Hong Kong, Urumqi change
|11:00 - 11:22|
|11:22 - 11:45|
Ali GhanbariThe University of Texas at Dallas, Samuel BentonThe University of Texas at Dallas, Lingming ZhangPre-print
|11:45 - 12:07|
Kui Liu, Anil KoyuncuUniversity of Luxembourg, Luxembourg, Dongsun KimFuriosa.ai, Tegawendé F. BissyandéSnT, University of LuxembourgPre-print
|12:07 - 12:30|