The DOI matches its OpenAlex record; 1 of the 1 references it cites were found in a registry or an index.
Visualization of test information to assist fault localization
Abstract
One of the most expensive and time-consuming components of the debugging process is locating the errors or faults. To locate faults, developers must identify statements involved in failures and select suspicious statements that might contain faults. This paper presents a new technique that uses visualization to assist with these tasks. The technique uses color to visually map the participation of each program statement in the outcome of the execution of the program with a test suite, consisting of both passed and failed test cases. Based on this visual mapping, a user can inspect the statements in the program, identify statements involved in failures, and locate potentially faulty statements. The paper also describes a prototype tool that implements our technique along with a set of empirical studies that use the tool for evaluation of the technique. The empirical studies show that, for the subject we studied, the technique can be effective in helping a user locate faults in a program.
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Citations
Cites
Expertise in debugging computer programs: A process analysis
Vessey, I. (1985) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(85)80054-7 · Cited by 359
Cited by
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.103 · Cited by 1,154
A Survey on Software Fault Localization
Wong, W. E., Gao, R., Li, Y., Abreu, R., & Wotawa, F. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2521368 · Cited by 1,110
SOBER: statistical model-based bug localization
Liu, C., Yan, X., Fei, L., Han, J., & Midkiff, S. P. (2005) · Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering · doi:10.1145/1081706.1081753 · Cited by 357
AutoCodeRover: Autonomous Program Improvement
Zhang, Y., Ruan, H., Fan, Z., & Roychoudhury, A. (2024) · Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3650212.3680384 · Cited by 202
AutoCodeRover: Autonomous Program Improvement
Zhang, Y., Ruan, H., Fan, Z., & Roychoudhury, A. (2024) · arXiv · doi:10.48550/arxiv.2404.05427 · Cited by 202
Metadata
- Authors
- Jones, J. A., Harrold, M. J., & Stasko, J.
- Venue
- Proceedings/Proceedings - International Conference on Software Engineering
- Publisher
- Institute of Electrical and Electronics Engineers
- Published
- 2002-01-01
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2162045655
- Cited by
- 904
- References
- 1
- FWCI
- 13.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Jones, J. A., Harrold, M. J., & Stasko, J. (2002). Visualization of test information to assist fault localization. Proceedings/Proceedings - International Conference on Software Engineering. https://doi.org/10.1145/581339.581397
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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.