The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Pex–White Box Test Generation for .NET
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Citations
Cites
de Moura, L., & Bjørner, N. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-78800-3_24 · Cited by 6,595
Cited by
A survey of symbolic execution techniques
Baldoni, R., Coppa, E., D’elia, D. C., Demetrescu, C., & Finocchi, I. (2019) · ACM Computing Surveys · doi:10.1145/3182657 · Cited by 708
Fraser, G., & Arcuri, A. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.14 · Cited by 586
(2021) · doi:10.1109/tse.2019.2946563 · Cited by 574
Fairness testing: testing software for discrimination
Galhotra, S., Brun, Y., & Meliou, A. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106277 · Cited by 405
“Automated testing research has produced tools to generate tests, including random testing, such as Randoop [63, 64], NightHawk [4], JCrasher [20], CarFast [65], and T3 [69]; search-based testing, such as EvoSuite [29], TestFul [9], and eToc [78]; dynamic symbolic execution tools, such as DSC [37], Symbolic PathFinder…”
MethodShamshiri, S., Just, R., Rojas, J. M., Fraser, G., McMinn, P., & Arcuri, A. (2015) · 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2015.86 · Cited by 230
Dsc+Mock: a test case + mock class generator in support of coding against interfaces
Islam, M., & Csallner, C. (2010) · Proceedings of the Eighth International Workshop on Dynamic Analysis · doi:10.1145/1868321.1868326 · Cited by 30
Measuring code behavioral similarity for programming and software engineering education
Li, S., Xiao, X., Bassett, B., Xie, T., & Tillmann, N. (2016) · Proceedings of the 38th International Conference on Software Engineering Companion · doi:10.1145/2889160.2889204 · Cited by 30
Self-citationAre “Solved Issues” in SWE-bench Really Solved Correctly? An Empirical Study
Wang, Y., Pradel, M., & Liu, Z. (2026) · Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering · doi:10.1145/3744916.3764576 · Cited by 3
“We envision PatchDiff to be useful for building a sustainable and robust patch validation ecosystem for assessing issue-solving tools.”
Metadata
- Authors
- Tillmann, N., & de Halleux, J.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- Pages
- 134-153
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2110311336
- Cited by
- 791
- References
- 1
- FWCI
- 32.12
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
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.
