<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and improve productivity. This project introduces an adaptive ranking approach that leverages project knowledge through functional decomposition of source code, API descriptions of library components, the bugfixing history, the code change history, and the file dependency graph. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. I applied SVM (Support Virtual Machine) to classify the bug reports to identify, which category the bug belongs to. It helps to fix the critical defects early. The ranking system evaluated on six large scale open source Java projects, using the beforefix version of the project for every bug report. The experimental results show that the learning-to-rank approach outperforms three recent state-of-the-art methods. In particular, proposed method makes correct recommendations within the top 10 ranked source files for over 70 percent of the bug reports in the Eclipse Platform and Tomcat projects.</p>
</abstract>

When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and improve productivity. This project introduces an adaptive ranking approach that leverages project knowledge through functional decomposition of source code, API descriptions of library components, the bugfixing history, the code change history, and the file dependency graph. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. I applied SVM (Support Virtual Machine) to classify the bug reports to identify, which category the bug belongs to. It helps to fix the critical defects early. The ranking system evaluated on six large scale open source Java projects, using the beforefix version of the project for every bug report. The experimental results show that the learning-to-rank approach outperforms three recent state-of-the-art methods. In particular, proposed method makes correct recommendations within the top 10 ranked source files for over 70 percent of the bug reports in the Eclipse Platform and Tomcat projects.

Learning to Rank and Classification of Bug Reports Using SVM and Feature Evaluation

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and...

Learning to Rank and Classification of Bug Reports Using SVM and Feature Evaluation

Pubblicato online: 01 set 2017

Pagine: 311 - 329

Ricevuto: 27 mag 2017

Accettato: 15 giu 2017

DOI: https://doi.org/10.21307/ijssis-2017-254

© 2017 Chandra Khatua et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.