Privacy preserving via interval covering based subclass division and manifold learning based bi-directional obfuscation for effort estimation

Qi, Fumin; Jing, Xiao‐Yuan; Zhu, Xiaoke; Wu, Fei; Cheng, Li

doi:10.1145/2970276.2970302

Cited by 4 publications

(3 citation statements)

References 53 publications

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“…Recently, privacy preservation issue has been investigated in some software engineering applications, e.g. software defect prediction [64, 114], software effort estimation [115] and so on.…”

Section: Manipulating the Datamentioning

confidence: 99%

“…Furthermore, studies on heterogeneous cross‐project prediction feasibility are not mature yet in practice. At this point, there remains as an open research area for practical use of heterogeneous cross‐project defect prediction. Privacy preservation issue: Due to the business sensitivity and privacy concerns, most companies are not willing to share their data [64, 114–116]. In this scenario, it is often difficult to extract data from industrial companies.…”

Section: Future Directions and Challengesmentioning

confidence: 99%

“…Privacy preservation issue: Due to the business sensitivity and privacy concerns, most companies are not willing to share their data [64, 114–116]. In this scenario, it is often difficult to extract data from industrial companies.…”

Section: Future Directions and Challengesmentioning

confidence: 99%

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Progress on approaches to software defect prediction

2018

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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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Section: Manipulating the Datamentioning

confidence: 99%