SPAN: A self-paced association augmentation and node embedding-based model for software bug classification and assignment

2021 ◽  
pp. 107711
Author(s):  
Hufsa Mohsin ◽  
Chongyang Shi ◽  
Shufeng Hao ◽  
He Jiang
Keyword(s):  
Software Bug

Multi-label software bug categorisation based on fuzzy similarity

2021 ◽  
Vol 24 (3) ◽  
pp. 244
Author(s):  
Rama Ranjan Panda ◽  
Naresh Kumar Nagwani
Keyword(s):  
Fuzzy Similarity ◽  
Software Bug

Continuous Software Bug Prediction

2021 ◽  
Author(s):  
Song Wang ◽  
Junjie Wang ◽  
Jaechang Nam ◽  
Nachiappan Nagappan
Keyword(s):  
Software Bug Prediction ◽  
Software Bug

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.

Visualizing the Bug Distribution Information Available in Software Bug Repositories

2014 ◽  
pp. 48-66
Author(s):  
N. K. Nagwani ◽  
S. Verma
Keyword(s):  
Open Source ◽  
Three Dimensional ◽  
Distribution Patterns ◽  
Two Dimensional ◽  
Software Bugs ◽  
Process Visualization ◽  
Software Information ◽  
Visualization Techniques ◽  
Software Bug ◽  
Bug Repositories

Software repositories contain a wealth of information that can be analyzed for knowledge extraction. Software bug repositories are one such repository that stores the information about the defects identified during the development of software. Information available in software bug repositories like number of bugs priority-wise, component-wise, status-wise, developers-wise, module-wise, summary-terms-wise, can be visualized with the help of two- or three-dimensional graphs. These visualizations help in understanding the bug distribution patterns, software matrices related to the software bugs, and developer information in the bug-fixing process. Visualization techniques are exploited with the help of open source technologies in this chapter to visualize the bug distribution information available in the software bug repositories. Two-dimensional and three-dimensional graphs are generated using java-based open source APIs, namely Jzy3d (Java Easy 3d) and JFreeChart. Android software bug repository is selected for the experimental demonstrations of graphs. The textual bug attribute information is also visualized using frequencies of frequent terms present in it.

Impact of Software Bug Report Preprocessing and Vectorization on Bug Assignment Accuracy

2021 ◽  
pp. 153-162
Author(s):  
Lukasz Chmielowski ◽  
Michal Kucharzak
Keyword(s):  
Assignment Accuracy ◽  
Bug Report ◽  
Software Bug
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