The DOI matches its Crossref record; 137 of the 137 references it cites were found in a registry or an index.
Progress on approaches to software defect prediction
Abstract
Software defect prediction is one of the most popular research topics in software engineering. It aims to predict defect‐prone software modules before defects are discovered, therefore it can be used to better prioritise software quality assurance effort. In recent years, especially for recent 3 years, many new defect prediction studies have been proposed. The goal of this study is to comprehensively review, analyse and discuss the state‐of‐the‐art of defect prediction. The authors survey almost 70 representative defect prediction papers in recent years (January 2014–April 2017), most of which are published in the prominent software engineering journals and top conferences. The selected defect prediction papers are summarised to four aspects: machine learning‐based prediction algorithms, manipulating the data, effort‐aware prediction and empirical studies. The research community is still facing a number of challenges for building methods and many research opportunities exist. The identified challenges can give some practical guidelines for both software engineering researchers and practitioners in future software defect prediction.
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A Systematic Literature Review on Fault Prediction Performance in Software Engineering
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Use of relative code churn measures to predict system defect density
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Predicting Defects for Eclipse
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On the relative value of cross-company and within-company data for defect prediction
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Metadata
- Authors
- Li, Z., Jing, X.-Y., & Zhu, X.
- Venue
- IET Software
- Published
- 2018-06
- Volume
- 12
- Issue
- 3
- Pages
- 161-175
- ISSN
- 1751-8806 (ISSN-L)
- OpenAlex
- W2791812915
- Cited by
- 264
- References
- 137
- FWCI
- 32.54
- Type
- journal article
- Language
- en
- Open access
- closed
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