文件信息增强的方法级软件缺陷定位

薄莉莉; 朱程; 李斌; 孙小兵

doi:10.12263/DZXB.20210878

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文件信息增强的方法级软件缺陷定位

学术论文 | 更新时间：2025-12-08

- 文件信息增强的方法级软件缺陷定位
- Method-Level Software Bug Localization Enhanced with File Information
- 电子学报 2023年51卷第3期页码：613-621
- 作者机构：
  
  1.扬州大学信息工程学院,江苏扬州 225127
  2.高安全系统的软件开发与验证技术工业和信息化部重点实验室,江苏南京 211106
- 作者简介：
  
  [ "薄莉莉女， 1989年10月出生于山东省泰安市. 现为扬州大学信息工程学院讲师，从事软件测试方面的研究工作.E-mail: lilibo@yzu.edu.cn" ]
  [ "朱程男， 1997年11月出生于江苏省扬州市，现为扬州大学信息工程学院硕士研究生，主要研究方向为软件缺陷定位. E-mail: zhucheng0a@gmail.com" ]
  [ "李斌（通讯作者）男， 1965年12月出生于江苏省扬州市. 现为扬州大学信息工程学院教授、博士生导师. 主要研究方向为软件工程. 在国内外发表学术论文150余篇." ]
  [ "孙小兵男， 1985年9月出生于江苏省泰州市. 现为扬州大学信息工程学院教授. 主要研究方向为智能化软件工程. 在国内外发表学术论文80余篇. E-mail: xbsun@yzu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61872312;61972335;62002309);南京航空航天大学科研基地创新（理工类）项目(NJ2020022);扬州大学“高端人才支持计划”(2019);江苏省“六大人才高峰”高层次人才项目(RJFW-053);江苏省“333”工程中青年科学技术带头人项目;扬州大学畜牧学学科特区学科交叉课题支持项目(yzuxk202015);Yangzhou University Interdisciplinary Research Foundation for Animal Husbandry Discipline of Targeted Support(yzuxk202015)
- DOI：10.12263/DZXB.20210878
  中图分类号： TP311.5
- 收稿：2021-07-09，
  
  修回：2021-12-20，
  
  纸质出版：2023-03-25
- 稿件说明：
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薄莉莉,朱程,李斌等.文件信息增强的方法级软件缺陷定位[J].电子学报,2023,51(03):613-621.

BO Li-li,ZHU Cheng,LI Bin,et al.Method-Level Software Bug Localization Enhanced with File Information[J].ACTA ELECTRONICA SINICA,2023,51(03):613-621.
薄莉莉,朱程,李斌等.文件信息增强的方法级软件缺陷定位[J].电子学报,2023,51(03):613-621. DOI： 10.12263/DZXB.20210878.

BO Li-li,ZHU Cheng,LI Bin,et al.Method-Level Software Bug Localization Enhanced with File Information[J].ACTA ELECTRONICA SINICA,2023,51(03):613-621. DOI： 10.12263/DZXB.20210878.

摘要

软件开发与维护中会产生大量缺陷报告，根据缺陷报告准确定位到缺陷代码的位置是极具挑战性的.目前大多数工作在文件粒度定位缺陷，虽然少量工作定位在方法粒度，但定位准确度较低. 本文提出一个文件信息增强的方法级软件缺陷定位技术FMBL（a File information enhanced Method-level Bug Localization technology），考虑方法与文件之间的从属关系以增强缺陷定位准确性. 通过综合考虑代码与缺陷报告的词汇相似度、语义相似度和代码长度度量它们之间的相关性. 在六个开源软件项目上开展实验以评估FMBL的有效性. 结果表明，本文方法在六个项目上的平均Accuracy@10、MAP（Mean Average Precision）和MRR（Mean Reciprocal Rank）值分别达到0.436、0.223、0.296.与现有方法BugLocator、BLIA（Bug Localization using Integrated Analysis）、BugPecker相比，本文方法在MAP指标上分别提升153.1%、209.1%、22.8%.

Abstract

A large number of bug reports are generated during software development and maintenance. Finding the location of the bug code based on the bug report is a challenging task. Most of the bug localization works focus on file granularity. Even though few works locate bugs in method granularity

the accuracy is relatively low. In this paper

we proposed FMBL (a File information enhanced Method-level Bug Localization technology)

a method-level software bug localization approach enhanced with file information. It considers the affiliation between methods and files to enhance the accuracy of bug localization. Lexical and semantic similarity between code and bug reports

together with code size are used to measure the correlation of methods and files. We conduct experiments on six open source software projects to evaluate the effectiveness of FMBL. The results show that

with FMBL

the average Accuracy@10

MAP (Mean Average Precision)

and MRR(Mean Reciprocal Rank) on the six projects can reach 0.436

0.223 and 0.296

respectively. Compared with BugLocator

BLIA (Bug Localization using Integrated Analysis) and BugPecker

our approach improves by 153.1%

209.1% and 22.8% in MAP

respectively.

关键词

Keywords

references

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