The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Randoop: feedback-directed random testing for Java
Abstract
R ANDOOP for Java generates unit tests for Java code using feedback-directed random test generation. Below we describe R ANDOOP 's input, output, and test generation algorithm. We also give an overview of RANDOOP's annotation-based interface for specifying configuration parameters that affect R ANDOOP 's behavior and output.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
JCrasher: an automatic robustness tester for Java
Csallner, C., & Smaragdakis, Y. (2004) · Software: Practice and Experience · doi:10.1002/spe.602 · Cited by 426
Cited by
Large language models for software engineering: survey and open problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) · doi:10.1109/icse-fose59343.2023.00008 · Cited by 650
Fraser, G., & Arcuri, A. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.14 · Cited by 586
Large Language Models for Software Engineering: Survey and Open Problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · arXiv · doi:10.48550/arxiv.2310.03533 · Cited by 523
FairFuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage
Lemieux, C., & Sen, K. (2018) · Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering · doi:10.1145/3238147.3238176 · Cited by 477
An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
Schäfer, M., Nadi, S., Eghbali, A., & Tip, F. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3334955 · Cited by 431
“They position their technique as a complement to traditional techniques [8], [17].”
BackgroundMethodInfluentialFairness 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
Shamshiri, 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
Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction
Kang, S., Yoon, J., & Yoo, S. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00194 · Cited by 204
Identifying patch correctness in test-based program repair
Xiong, Y., Liu, X., Zeng, M., Zhang, L., & Huang, G. (2018) · Proceedings of the 40th International Conference on Software Engineering · doi:10.1145/3180155.3180182 · Cited by 162
DocTer: documentation-guided fuzzing for testing deep learning API functions
Xie, D., Li, Y., Kim, M., Pham, H. V., Tan, L., Zhang, X., & Godfrey, M. W. (2022) · Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3533767.3534220 · Cited by 81
Code Review Automation: Strengths and Weaknesses of the State of the Art
Tufano, R., Dabić, O., Mastropaolo, A., Ciniselli, M., & Bavota, G. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3348172 · Cited by 48
Bhatia, S., Gandhi, T., Kumar, D., & Jalote, P. (2024) · Proceedings of the 1st International Workshop on Large Language Models for Code · doi:10.1145/3643795.3648396 · Cited by 55
Bayesian Data Analysis in Empirical Software Engineering Research
Furia, C. A., Feldt, R., & Torkar, R. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2935974 · Cited by 42
Opportunities and Risks of LLMs for Scalable Deliberation with Polis
Small, C. T., Vendrov, I., Durmus, E., Homaei, H., Barry, E., Cornebise, J., Suzman, T., Ganguli, D., & Megill, C. (2023) · arXiv · doi:10.48550/arxiv.2306.11932 · Cited by 20
A 2030 Roadmap for Software Engineering
Pezzè, M., Abrahão, S., Penzenstadler, B., Poshyvanyk, D., Roychoudhury, A., & Yue, T. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3731559 · Cited by 13
Are “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.”
Rethinking Verification for LLM Code Generation: From Generation to Testing
Ma, Z., Zhang, T., Cao, M., Liu, J., Zhang, W., Luo, M., Zhang, S., & Chen, K. (2025) · arXiv · doi:10.48550/arxiv.2507.06920 · Cited by 2
Metadata
- Authors
- Pacheco, C., & Ernst, M. D.
- Venue
- Companion to the 22nd ACM SIGPLAN conference on Object-oriented programming systems and applications companion
- Publisher
- ACM
- Published
- 2007-10-20
- Pages
- 815-816
- OpenAlex
- W1965194038
- Cited by
- 558
- References
- 1
- FWCI
- 2.83
- 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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.