The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Predicting Vulnerable Software Components via Text Mining
Open accessAbstract
This paper presents an approach based on machine learning to predict which components of a software application contain security vulnerabilities. The approach is based on text mining the source code of the components. Namely, each component is characterized as a series of terms contained in its source code, with the associated frequencies. These features are used to forecast whether each component is likely to contain vulnerabilities. In an exploratory validation with 20 Android applications, we discovered that a dependable prediction model can be built. Such model could be useful to prioritize the validation activities, e.g., to identify the components needing special scrutiny.
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Cites
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A systematic review of software fault prediction studies
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Predicting vulnerable software components
Neuhaus, S., Zimmermann, T., Holler, C., & Zeller, A. (2007) · Proceedings of the 14th ACM conference on Computer and communications security · doi:10.1145/1315245.1315311 · Cited by 416
On the value of static analysis for fault detection in software
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Cited by
Ghaffarian, S. M., & Shahriari, H. R. (2018) · ACM Computing Surveys · doi:10.1145/3092566 · Cited by 353
Vulnerability detection with fine-grained interpretations
Li, Y., Wang, S., & Nguyen, T. N. (2021) · Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3468264.3468597 · Cited by 282
Metadata
- Authors
- Scandariato, R., Walden, J., Hovsepyan, A., & Joosen, W.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2014-10-01
- Volume
- 40
- Issue
- 10
- Pages
- 993-1006
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- OpenAlex
- W2079753286
- Cited by
- 337
- References
- 28
- FWCI
- 44.38
- Type
- journal article
- Language
- en
- Open access
- green
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- Scandariato, R., Walden, J., Hovsepyan, A., & Joosen, W. (2014). Predicting Vulnerable Software Components via Text Mining. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2014.2340398
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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