Open AccessProgress on approaches to software defect prediction
Author(s) -
Li Zhiqiang,
Jing Xiao-Yuan,
Zhu Xiaoke
Publication year - 2018
Publication title -
iet software
Language(s) - English
Resource type - Journals
ISSN - 1751-8814
DOI - 10.1049/iet-sen.2017.0148
Subject(s) - computer science , software bug , software , software engineering , predictive modelling , software quality , data science , software quality assurance , software development , software peer review , software metric , verification and validation , software construction , machine learning , engineering , programming language , operations management
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.