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access icon free Progress on approaches to software defect prediction

  • Author(s): Zhiqiang Li 1 ; Xiao-Yuan Jing 1, 2 ; Xiaoke Zhu 1, 3
    • View affiliations
  • Source: Volume 12, Issue 3, June 2018, p. 161 – 175
    DOI:  10.1049/iet-sen.2017.0148 , Print ISSN 1751-8806, Online ISSN 1751-8814
© The Institution of Engineering and Technology
Received 22/06/2017, Accepted 10/02/2018, Revised 17/01/2018, Published 13/02/2018

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.

Inspec keywords: quality assurance; software quality; software reliability; research and development

Other keywords: software engineering journals; software defect prediction; machine learning-based prediction algorithms; software quality assurance; effort-aware prediction; data manipulation; defect-prone software modules; empirical studies

Subjects: Software management; Software engineering techniques

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