Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 250200024-14.doi: 10.11896/jsjkx.250200024

• Computer Software & Architecture • Previous Articles     Next Articles

Advances in Automatic Software Defect Location Techniques

FANG Jinqiu, YUN Guorong, ZHAO Haiyong, XIE Haomeng   

  1. College of Computer Science and Technology,Liaocheng University,Liaocheng,Shandong 252000,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    National Natural Science Foundation of China(62402202),Natural Science Foundation of Shandong Province,China(ZR2024QF036),Shandong Province Undergraduate Teaching Reform Research Project(M2023218) and 2024 National College Students’ Innovation and Entrepreneurship Training Program Project(202410447004).

Abstract: Software defect localization has become an important research topic in the field of software debugging,and automated software defect localization aims to improve the degree of automation of defect location,help developers locate possible defects in large-scale software more efficiently,so as to achieve the purpose of optimal allocation of test resources.With the defect location technology of automatic software as the core,the relevant research results are systematically sorted out.Firstly,according to whether it is necessary to run test cases,the positioning technology is divided into static positioning technology and dynamic positioning technology,the representative algorithms of each type of technology are summarized,and the influence of deep learning on related algorithms is discussed.Secondly,according to the difference in the number of defects contained in the software system,the related algorithms are compared from the perspectives of single defect and multiple defects.Finally,the commonly used evaluation datasets and evaluation indicators of related algorithms are given,some challenges in this field are pointed out,and some directions worthy of further research are prospected.

Key words: Fault localization, Defect detection, Software engineering, Software quality, Deep learning

CLC Number: 

  • TP311
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