Date Available

1-17-2017

Year of Publication

2017

Document Type

Master's Thesis

Degree Name

Master of Science (MS)

College

Engineering

Department/School/Program

Computer Science

Advisor

Dr. Jane Hayes

Co-Director of Graduate Studies

Dr. Tingting Yu

Abstract

In this study, a tool is developed that achieves two purposes: (1) given bug reports, it identifies configuration bug reports from non-configuration bug reports; (2) once a bug report is identified to be a configuration bug report, the tool finds out what specific configuration option the bug report is associated.

This study starts with a review of related works that used machine learning tools to solve software bug and bug report related issues. It then discusses the natural language processing and machine learning techniques. Afterwards, the development process of the proposed tool is described in detail, including the motivation, the experiment design and setup, and results analysis. In order to evaluate the effectiveness of the tool, both cross-validation and a similar validation technique are performed. Results show that the tool is effective at both identifying configuration bug reports and the associated configuration options for the identified bug reports.

This study proves the usefulness of machine learning techniques in solving bug report related issues. It also shows that configuration and non-configuration bug reports have different characteristics that can be learned by machine learning tools. The developed tool can be improved in a number of areas to make it more effective.

Digital Object Identifier (DOI)

https://doi.org/10.13023/ETD.2017.047

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