Feature Comparison for Automatic Bug Report Classification

2019 ◽  
pp. 69-78 ◽  
Author(s):  
Bancha Luaphol ◽  
Boonchoo Srikudkao ◽  
Tontrakant Kachai ◽  
Natthakit Srikanjanapert ◽  
Jantima Polpinij ◽  
...  
Keyword(s):  
Bug Report

scholarly journals Security Bug Report Usage for Software Vulnerability Research: A Systematic Mapping Study

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28471-28495
Author(s):  
Farzana Ahamed Bhuiyan ◽  
Md Bulbul Sharif ◽  
Akond Rahman
Keyword(s):  
Systematic Mapping Study ◽  
Mapping Study ◽  
Software Vulnerability ◽  
Systematic Mapping ◽  
Bug Report

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.

Duplicate Bug Report Detection by Using Sentence Embedding and Fine-tuning

2021 ◽  
Author(s):  
Haruna Isotani ◽  
Hironori Washizaki ◽  
Yoshiaki Fukazawa ◽  
Tsutomu Nomoto ◽  
Saori Ouji ◽  
...  
Keyword(s):  
Fine Tuning ◽  
Bug Report

EKD-BSP: Bug Report Severity Prediction by Extracting Keywords from Description

2021 ◽  
Author(s):  
Yanxin Jia ◽  
Xiang Chen ◽  
Shuyuan Xu ◽  
Guang Yang ◽  
Jinxin Cao
Keyword(s):  
Bug Report ◽  
Severity Prediction

Mining Bug Report Repositories to Identify Significant Information for Software Bug Fixing

2021 ◽  
Author(s):  
Bancha Luaphol ◽  
Jantima Polpinij ◽  
Manasawee Kaenampornpan
Keyword(s):  
The Other ◽  
Problem Domain ◽  
Significant Information ◽  
Bug Reports ◽  
Bug Fixing ◽  
Classification Technique ◽  
Bug Report ◽  
Multiple Issues ◽  
Improved Accuracy ◽  
Software Bug

Most studies relating to bug reports aims to automatically identify necessary information from bug reports for software bug fixing. Unfortunately, the study of bug reports focuses only on one issue, but more complete and comprehensive software bug fixing would be facilitated by assessing multiple issues concurrently. This becomes a challenge in this study, where it aims to present a method of identifying bug reports at severe level from a bug report repository, together with assembling their related bug reports to visualize the overall picture of a software problem domain. The proposed method is called “mining bug report repositories”. Two techniques of text mining are applied as the main mechanisms in this method. First, classification is applied for identifying severe bug reports, called “bug severity classification”, while “threshold-based similarity analysis” is then applied to assemble bug reports that are related to a bug report at severe level. Our datasets are from three opensource namely SeaMonkey, Firefox, and Core:Layout downloaded from the Bugzilla. Finally, the best models from the proposed method are selected and compared with two baseline methods. For identifying severe bug reports using classification technique, the results show that our method improved accuracy, F1, and AUC scores over the baseline by 11.39, 11.63, and 19% respectively. Meanwhile, for assembling related bug reports using threshold-based similarity technique, the results show that our method improved precision, and likelihood scores over the other baseline by 15.76, and 9.14% respectively. This demonstrate that our proposed method may help increasing chance to fix bugs completely.

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