Machine learning-based methods for identifying bug severity level from bug reports

Name: Kamthon Sarawan

Name: Jantima Polpinij

Name: Bancha Luaphol

LCSH: Software failures -- Data processing

LCSH: Machine learning

Abstract: Bug reports constitute an important source of information that may be utilized to remedy bugs. One aspect of bug report analysis is determining the severity level of the reported problem. Nevertheless, manually assigning severity to a large number of bug reports is time-consuming, and human analysis of the severity of soft-ware bugs may be subject to biased. Consequently, automated analysis is required for detecting the severity of software defects concealed inside bug reports. Even though there have been several attempts to tackle the problem and a number of machine learning and text mining approaches have been applied, the performance of the solu-tions may vary depending on the datasets employed. As a result, this is the objective of our research, in which we intend to evaluate the efficacy of many machine learning approaches (i.e. Logistic Regression, Random Forest, Support Vector Machine, and Long Short-Term Memory) for severity analysis on multiple bug report datasets (i.e. Core, Firefox, Thunderbird, and Bugzilla) from the Mozilla bug tracking system. It should be emphasized that our research is based on a binary classification scheme in which bug reports are classified into two classes: severe class and non-severe class. The experimental results revealed that the LR algorithm performed well in analyzing the severity of bug reports in the Core, Firefox, and Bugzilla datasets, whereas the LSTM approach performed well for the Thunderbird dataset

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2023

Modified: 2023-10-09

Issued: 2023-10-09

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BibliograpyCitation : In King Mongkut's University of Technology North Bangkok. Faculty of Information Technology and Digital Innovation. The 19th International Conference on Computing and Information Technology (IC2IT 2023) (pp.199-208). Bangkok : King Mongkut's University of Technology North Bangkok

eng

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