Software defect prediction: future directions and challenges

被引：7
作者：
Li, Zhiqiang [1 ]
Niu, Jingwen [2 ]
Jing, Xiao-Yuan [3 ,4 ]
机构：
[1] Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China
[2] Xinxiang Univ, Sch Comp & Informat Engn, Xinxiang 453003, Peoples R China
[3] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China
[4] Guangdong Univ Petrochem Technol, Sch Comp, Maoming 525000, Peoples R China
来源：
AUTOMATED SOFTWARE ENGINEERING | 2024年 / 31卷 / 01期
关键词：
Software defect prediction; Empirical software engineering; Software analytics; Quality assurance; FRAMEWORK; MODELS; CODE;
D O I：
10.1007/s10515-024-00424-1
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Software defect prediction is one of the most popular research topics in software engineering. The objective of defect prediction is to identify defective instances prior to the occurrence of software defects, thus it aids in more effectively prioritizing software quality assurance efforts. In this article, we delve into various prospective research directions and potential challenges in the field of defect prediction. The aim of this article is to propose a range of defect prediction techniques and methodologies for the future. These ideas are intended to enhance the practicality, explainability, and actionability of the predictions of defect models.
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页数：14
相关论文
共 71 条
  • [1] Bird C., 2011, P 19 ACM SIGSOFT S 1, P4, DOI DOI 10.1145/2025113.2025119
  • [2] Neto EC, 2018, 2018 25TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING (SANER 2018), P380, DOI 10.1109/SANER.2018.8330225
  • [3] An Empirical Study on Heterogeneous Defect Prediction Approaches
    Chen, Haowen
    Jing, Xiao-Yuan
    Li, Zhiqiang
    Wu, Di
    Peng, Yi
    Huang, Zhiguo
    [J]. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2021, 47 (12) : 2803 - 2822
  • [4] A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes
    da Costa, Daniel Alencar
    McIntosh, Shane
    Shang, Weiyi
    Kulesza, Uira
    Coelho, Roberta
    Hassan, Ahmed E.
    [J]. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2017, 43 (07) : 641 - 657
  • [5] Tuning for software analytics: Is it really necessary?
    Fu, Wei
    Menzies, Tim
    Shen, Xipeng
    [J]. INFORMATION AND SOFTWARE TECHNOLOGY, 2016, 76 : 135 - 146
  • [6] Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
    Ghotra, Baljinder
    McIntosh, Shane
    Hassan, Ahmed E.
    [J]. 2015 IEEE/ACM 37TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, VOL 1, 2015, : 789 - 800
  • [7] On the use of deep learning in software defect prediction
    Giray, Gorkem
    Bennin, Kwabena Ebo
    Koksal, Omer
    Babur, Onder
    Tekinerdogan, Bedir
    [J]. JOURNAL OF SYSTEMS AND SOFTWARE, 2023, 195
  • [8] Code-line-level Bugginess Identification: How Far haveWe Come, and How Far haveWe Yet to Go?
    Guo, Zhaoqiang
    Liu, Shiran
    Liu, Xutong
    Lai, Wei
    Ma, Mingliang
    Zhang, Xu
    Ni, Chao
    Yang, Yibiao
    Li, Yanhui
    Chen, Lin
    Zhou, Guoqiang
    Zhou, Yuming
    [J]. ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 2023, 32 (04)
  • [9] A Systematic Literature Review on Fault Prediction Performance in Software Engineering
    Hall, Tracy
    Beecham, Sarah
    Bowes, David
    Gray, David
    Counsell, Steve
    [J]. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2012, 38 (06) : 1276 - 1304
  • [10] Explainable Software Analytics
    Hoa Khanh Dam
    Truyen Tran
    Ghose, Aditya
    [J]. 2018 IEEE/ACM 40TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: NEW IDEAS AND EMERGING TECHNOLOGIES RESULTS (ICSE-NIER), 2018, : 53 - 56
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