The DOI matches its Crossref record.
DART: directed automated random testing
Abstract
We present a new tool, named DART, for automatically testing software that combines three main techniques: (1) automated extraction of the interface of a program with its external environment using static source-code parsing; (2) automatic generation of a test driver for this interface that performs random testing to simulate the most general environment the program can operate in; and (3) dynamic analysis of how the program behaves under random testing and automatic generation of new test inputs to direct systematically the execution along alternative program paths. Together, these three techniques constitute Directed Automated Random Testing , or DART for short. The main strength of DART is thus that testing can be performed completely automatically on any program that compiles -- there is no need to write any test driver or harness code. During testing, DART detects standard errors such as program crashes, assertion violations, and non-termination. Preliminary experiments to unit test several examples of C programs are very encouraging.
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
Cited by
The oracle problem in software testing: A survey
Barr, E. T., Harman, M., McMinn, P., Shahbaz, M., & Yoo, S. (2015) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2014.2372785 · Cited by 1,104
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
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
Luo, L., Ming, J., Wu, D., Liu, P., & Zhu, S. (2014) · Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering · doi:10.1145/2635868.2635900 · Cited by 220
Brubaker, 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
Luo, L., Ming, J., Wu, D., Liu, P., & Zhu, S. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2655046 · Cited by 144
Hard-to-answer questions about code
LaToza, T. D., & Myers, B. A. (2010) · Evaluation and Usability of Programming Languages and Tools · doi:10.1145/1937117.1937125 · Cited by 141
Validating SMT solvers via semantic fusion
Winterer, D., Zhang, C., & Su, Z. (2020) · Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/3385412.3385985 · Cited by 82
Program Logic Based Software Plagiarism Detection
Zhang, F., Wu, D., Liu, P., & Zhu, S. (2014) · 2014 IEEE 25th International Symposium on Software Reliability Engineering · doi:10.1109/issre.2014.18 · Cited by 57
CarFast: achieving higher statement coverage faster
Park, S., Hossain, B. M. M., Hussain, I., Csallner, C., Grechanik, M., Taneja, K., Fu, C., & Xie, Q. (2012) · Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering · doi:10.1145/2393596.2393636 · Cited by 55
(2008) · Cited by 47
Ming, J., Zhang, F., Wu, D., Liu, P., & Zhu, S. (2016) · IEEE Transactions on Reliability · doi:10.1109/tr.2016.2570554 · Cited by 33
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
Delta execution for efficient state-space exploration of object-oriented programs
d'Amorim, M., Lauterburg, S., & Marinov, D. (2007) · Proceedings of the 2007 international symposium on Software testing and analysis · doi:10.1145/1273463.1273472 · Cited by 26
Kuhn, R., Kacker, R. N., Lei, Y., & Simos, D. (2020) · Computer · doi:10.1109/mc.2019.2951980 · Cited by 26
Generating Unit Tests for Documentation
Nassif, M., Hernandez, A., Sridharan, A., & Robillard, M. P. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3087087 · Cited by 22
Metadata
- Authors
- Godefroid, P., Klarlund, N., & Sen, K.
- Venue
- ACM SIGPLAN Notices
- Published
- 2005-06-12
- Volume
- 40
- Issue
- 6
- Pages
- 213-223
- ISSN
- 0362-1340 (ISSN-L)
- 1558-1160
- OpenAlex
- W2096449544
- Cited by
- 2,067
- References
- 0
- FWCI
- 223.62
- Type
- journal article
- 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.
