Bug localization via searching crowd-contributed code

A deep understanding of the common patterns of bug-fixing changes is useful in several ways: (a) such knowledge can help developers in proactively avoiding coding patterns that lead to bugs and (b) bug-fixing patterns are exploited in devising techniques for automatic bug localization and program repair. This work includes an in-depth quantitative and qualitative analysis over 4,653 buggy revisions of five software systems. Our study identifies 38 bug-fixing edit patterns and discovers 37 new patterns of nested code structures, which frequently host the bug-fixing edits. While some of the edit patterns were reported in earlier studies, these nesting patterns are new and were never targeted before.

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Are datasets for information retrieval-based bug localization techniques trustworthy?

Empirical Software Engineering ◽

10.1007/s10664-021-09946-8 ◽

2021 ◽

Vol 26 (3) ◽

Author(s):

Misoo Kim ◽

Eunseok Lee

Keyword(s):

Information Retrieval ◽

Bug Localization

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Scaffle: bug localization on millions of files

Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis ◽

10.1145/3395363.3397356 ◽

2020 ◽

Author(s):

Michael Pradel ◽

Vijayaraghavan Murali ◽

Rebecca Qian ◽

Mateusz Machalica ◽

Erik Meijer ◽

...

Keyword(s):

Bug Localization

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Augmenting Bug Localization with Part-of-Speech and Invocation

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194017500346 ◽

2017 ◽

Vol 27 (06) ◽

pp. 925-949 ◽

Cited By ~ 5

Author(s):

Yu Zhou ◽

Yanxiang Tong ◽

Taolue Chen ◽

Jin Han

Keyword(s):

Software Maintenance ◽

Large Scale ◽

Bug Localization ◽

Bug Reports ◽

Part Of Speech ◽

Adaptive Technique ◽

Bug Report ◽

Software Maintenance And Evolution ◽

Speech Features ◽

Localization Approach

Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source-file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of the approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e. ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat.

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