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TimeAware test suite prioritization

Walcott, Kristen R., Soffa, Mary Lou, Kapfhammer, Gregory M., Roos, Robert S.·2006·Proceedings of the 2006 international symposium on Software testing and analysis

Abstract

Regression test prioritization is often performed in a time constrained execution environment in which testing only occurs for a fixed time period. For example, many organizations rely upon nightly building and regression testing of their applications every time source code changes are committed to a version control repository. This paper presents a regression test prioritization technique that uses a genetic algorithm to reorder test suites in light of testing time constraints. Experiment results indicate that our prioritization approach frequently yields higher average percentage of faults detected (APFD) values, for two case study applications, when basic block level coverage is used instead of method level coverage. The experiments also reveal fundamental trade offs in the performance of time-aware prioritization. This paper shows that our prioritization technique is appropriate for many regression testing environments and explains how the baseline approach can be extended to operate in additional time constrained testing circumstances.

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Metadata

Authors
Walcott, K. R., Soffa, M. L., Kapfhammer, G. M., & Roos, R. S.
Venue
Proceedings of the 2006 international symposium on Software testing and analysis
Publisher
ACM
Published
2006-07-21
Pages
1-12
OpenAlex
W2126752493
Cited by
340
References
0
FWCI
10.34
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: TimeAware test suite prioritization. https://app.cochat.ai/papers/10.1145/1146238.1146240
The paper
Walcott, K. R., Soffa, M. L., Kapfhammer, G. M., & Roos, R. S. (2006). TimeAware test suite prioritization. Proceedings of the 2006 international symposium on Software testing and analysis. https://doi.org/10.1145/1146238.1146240

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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.