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Continuous Integration (CI) is the process of auto-matically compiling, building, and testing code changes in the hope of catching bugs as they are introduced into the code base. With bug fixing being a core and increasingly costly task in software development, the community has adopted CI to mitigate this issue and improve the quality of their software products. Bug fixing is a core task in software development and becomes increasingly costly over time. However, little is known about how effective CI is at detecting simple, single-statement bugs. In this paper, we analyze the effectiveness of CI in 14 popular open source Java-based projects to warn about 318 single-statement bugs (SStuBs). We analyze the build status at the commits that introduce SStuBs and before the SStuBs were fixed. We then investigate how often CI indicates the presence of these bugs, through test failure. Our results show that only 2% of the commits that introduced SStuBs have builds with failed tests and 7.5% of builds before the fix reported test failures. Upon close manual inspection, we found that none of the failed builds actually captured SStuBs, indicating that CI is not the right medium to capture the SStuBs we studied. Our results suggest that developers should not rely on CI to catch SStuBs or increase their CI pipeline coverage to detect single-statement bugs.
Software Engineering, Continuous Integration
Software Engineering, Continuous Integration
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 5 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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