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Finding bugs is easy
Abstract
Many techniques have been developed over the years to automatically find bugs in software. Often, these techniques rely on formal methods and sophisticated program analysis. While these techniques are valuable, they can be difficult to apply, and they aren't always effective in finding real bugs. Bug patterns are code idioms that are often errors. We have implemented automatic detectors for a variety of bug patterns found in Java programs. In this paper, we describe how we have used bug pattern detectors to find serious bugs in several widely used Java applications and libraries. We have found that the effort required to implement a bug pattern detector tends to be low, and that even extremely simple detectors find bugs in real applications.From our experience applying bug pattern detectors to real programs, we have drawn several interesting conclusions. First, we have found that even well tested code written by experts contains a surprising number of obvious bugs. Second, Java (and similar languages) have many language features and APIs which are prone to misuse. Finally, that simple automatic techniques can be effective at countering the impact of both ordinary mistakes and misunderstood language features.
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Cited by
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Learning a Metric for Code Readability
Buse, R. P. L., & Weimer, W. R. (2010) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2009.70 · Cited by 396
A systematic literature review of automated feedback generation for programming exercises
Keuning, H., Jeuring, J., & Heeren, B. (2019) · ACM Transactions on Computing Education · doi:10.1145/3231711 · Cited by 370
Lessons from building static analysis tools at Google
Sadowski, C., Aftandilian, E., Eagle, A., Miller-Cushon, L., & Jaspan, C. (2018) · Communications of the ACM · doi:10.1145/3188720 · Cited by 242
JEMMA: An extensible Java dataset for ML4Code applications
Karmakar, A., Allamanis, M., & Robbes, R. (2023) · Empirical Software Engineering · doi:10.1007/s10664-022-10275-7 · Cited by 4
Metadata
- Authors
- Hovemeyer, D., & Pugh, W.
- Venue
- ACM SIGPLAN Notices
- Published
- 2004-12
- Volume
- 39
- Issue
- 12
- Pages
- 92-106
- ISSN
- 0362-1340 (ISSN-L)
- 1558-1160
- OpenAlex
- W2087527532
- Cited by
- 397
- References
- 0
- FWCI
- 29.14
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
