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Cited by 689

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Automatically learning semantic features for defect prediction

Wang, Song, Liu, Taiyue, Tan, Lin·2016·Proceedings of the 38th International Conference on Software Engineering
DOI 10.1145/2884781.2884804
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Abstract

Software defect prediction, which predicts defective code regions, can help developers find bugs and prioritize their testing efforts. To build accurate prediction models, previous studies focus on manually designing features that encode the characteristics of programs and exploring different machine learning algorithms. Existing traditional features often fail to capture the semantic differences of programs, and such a capability is needed for building accurate prediction models.

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Cited by

Metadata

Authors
Wang, S., Liu, T., & Tan, L.
Venue
Proceedings of the 38th International Conference on Software Engineering
Publisher
ACM
Published
2016-05-14
Pages
297-308
ISSN
0270-5257 (ISSN-L)
DOI
10.1145/2884781.2884804
OpenAlex
W2360967250
Cited by
689
References
3
FWCI
51.57
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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