The DOI matches its Crossref record; 115 of the 115 references it cites were found in a registry or an index.
An orchestrated survey of methodologies for automated software test case generation
Open accessAbstract
Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is contributed by world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a perspective of the future development of the approach. As a whole, the paper aims at giving an introductory, up-to-date and (relatively) short overview of research in automatic test case generation, while ensuring a comprehensive and authoritative treatment.
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
de Moura, L., & Bjørner, N. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-78800-3_24 · Cited by 6,595
Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980
Software Product Lines: Practices and Patterns
Clements, P., & Northrop, L. (2001) · Cited by 3,232
KLEE: unassisted and automatic generation of high-coverage tests for complex systems programs
Cadar, C., Dunbar, D., & Engler, D. (2008) · doi:10.5555/1855741.1855756 · Cited by 2,620
The B-book: assigning programs to meanings
(1997) · Choice Reviews Online · doi:10.5860/choice.34-3906 · Cited by 2,106
DART: directed automated random testing
Godefroid, P., Klarlund, N., & Sen, K. (2005) · ACM SIGPLAN Notices · doi:10.1145/1064978.1065036 · Cited by 2,067
Cochran, W. G., & Cox, G. M. (1957) · Cited by 1,729
CUTE: a concolic unit testing engine for C
Sen, K., Marinov, D., & Agha, G. (2005) · Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering · doi:10.1145/1081706.1081750 · Cited by 1,651
DART: directed automated random testing
Godefroid, P., Klarlund, N., & Sen, K. (2005) · Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation · doi:10.1145/1065010.1065036 · Cited by 1,570
Search‐based software test data generation: a survey
McMinn, P. (2004) · Software Testing, Verification and Reliability · doi:10.1002/stvr.294 · Cited by 1,368
Self-citation
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
Self-citation(2021) · doi:10.1109/tse.2019.2946563 · Cited by 574
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
Self-citationBrubaker, C., Jana, S., Ray, B., Khurshid, S., & Shmatikov, V. (2014) · 2014 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2014.15 · Cited by 218
AI system engineering key challenges and lessons learned
Fischer, L., Ehrlinger, L., Geist, V., Ramler, R., Sobiezky, F., Zellinger, W., Brunner, D., Kumar, M., & Moser, B. (2020) · Machine Learning and Knowledge Extraction · doi:10.3390/make3010004 · Cited by 71
“Quality assurance measures in software engineering include, for example, automated testing [131], static code analysis [132], system redocumentation [133], or symbolic execution [134].”
Method
Metadata
- Authors
- Anand, S., Burke, E. K., Chen, T. Y., Clark, J., Cohen, M. B., Grieskamp, W., Harman, M., Harrold, M. J., McMinn, P., Bertolino, A., Jenny Li, J., & Zhu, H.
- Venue
- Journal of Systems and Software
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 86
- Issue
- 8
- Pages
- 1978-2001
- ISSN
- 0164-1212 (ISSN-L)
- OpenAlex
- W1979345446
- Cited by
- 668
- References
- 115
- FWCI
- 158.63
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An orchestrated survey of methodologies for automated software test case generation. https://app.cochat.ai/papers/10.1016/j.jss.2013.02.061
- The paper
- Anand, S., Burke, E. K., Chen, T. Y., Clark, J., Cohen, M. B., Grieskamp, W., Harman, M., Harrold, M. J., McMinn, P., Bertolino, A., Jenny Li, J., & Zhu, H. (2013). An orchestrated survey of methodologies for automated software test case generation. Journal of Systems and Software. https://doi.org/10.1016/j.jss.2013.02.061
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.
