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Bandera: extracting finite-state models from Java source code

Open access
Corbett, James C., Dwyer, Matthew B., Hatcliff, John, Laubach, Shawn, Păsăreanu, Corina S., Robby, Zheng, Hongjun·2000·Proceedings of the 22nd international conference on Software engineering - ICSE '00

Abstract

Finite-state verification techniques, such as model checking, have shown promise as a cost-effective means for finding defects in hardware designs. To date, the application of these techniques to software has been hindered by several obstacles. Chief among these is the problem of constructing a finite-state model that approximates the executable behavior of the software system of interest. Current best-practice involves hand-construction of models which is expensive (prohibitive for all but the smallest systems), prone to errors (which can result in misleading verification results), and difficult to optimize (which is necessary to combat the exponential complexity of verification algorithms).

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Metadata

Authors
Corbett, J. C., Dwyer, M. B., Hatcliff, J., Laubach, S., Păsăreanu, C. S., Robby, & Zheng, H.
Venue
Proceedings of the 22nd international conference on Software engineering - ICSE '00
Publisher
ACM Press
Published
2000
Pages
439-448
ISSN
0270-5257 (ISSN-L)
OpenAlex
W2135274583
Cited by
1,059
References
0
FWCI
64.43
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Bandera: extracting finite-state models from Java source code. https://app.cochat.ai/papers/10.1145/337180.337234
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Corbett, J. C., Dwyer, M. B., Hatcliff, J., Laubach, S., Păsăreanu, C. S., Robby, & Zheng, H. (2000). Bandera: extracting finite-state models from Java source code. Proceedings of the 22nd international conference on Software engineering - ICSE '00. https://doi.org/10.1145/337180.337234

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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.