scholarly journals RTL bug localization through LTL specification mining (WIP)

2019 ◽  
Author(s):  
Vighnesh Iyer ◽  
Donggyu Kim ◽  
Borivoje Nikolic ◽  
Sanjit A. Seshia
Keyword(s):  
Bug Localization ◽  
Specification Mining

scholarly journals Tracking Buggy Files: New Efficient Adaptive Bug Localization Algorithm

2021 ◽  
pp. 1-1
Author(s):  
Mikolaj Marek Fejzer ◽  
Jakub Narebski ◽  
Piotr Marian Przymus ◽  
Krzysztof Stencel
Keyword(s):  
Localization Algorithm ◽  
Bug Localization

What changes in where?

2021 ◽  
Vol 20 (4) ◽  
pp. 18-34
Author(s):  
Md Rakibul Islam ◽  
Minhaz F. Zibran
Keyword(s):  
Qualitative Analysis ◽  
Deep Understanding ◽  
Software Systems ◽  
Bug Localization ◽  
Bug Fixing ◽  
Program Repair ◽  
The Common

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.

scholarly journals Adversarial Specification Mining

2021 ◽  
Vol 30 (2) ◽  
pp. 1-40
Author(s):  
Hong Jin Kang ◽  
David Lo
Keyword(s):  
Specification Mining

Scaffle: bug localization on millions of files

2020 ◽  
Author(s):  
Michael Pradel ◽  
Vijayaraghavan Murali ◽  
Rebecca Qian ◽  
Mateusz Machalica ◽  
Erik Meijer ◽  
...  
Keyword(s):  
Bug Localization

scholarly journals Augmenting Bug Localization with Part-of-Speech and Invocation

2017 ◽  
Vol 27 (06) ◽  
pp. 925-949 ◽  
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.

scholarly journals Network-Clustered Multi-Modal Bug Localization

2019 ◽  
Vol 45 (10) ◽  
pp. 1002-1023 ◽  
Author(s):  
Thong Hoang ◽  
Richard J. Oentaryo ◽  
Tien-Duy B. Le ◽  
David Lo
Keyword(s):  
Bug Localization
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