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Visualization of test information to assist fault localization

James A. Jones, Mary Jean Harrold, John Stasko·2002·Proceedings/Proceedings - International Conference on Software Engineering

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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OpenAlex · 10.1145/581339.581397

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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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CoChat. (2026). Citation check: Visualization of test information to assist fault localization. https://app.cochat.ai/papers/10.1145/581339.581397
The paper
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.