Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2016

Abstract

The configuration of a system determines the system behavior and wrong configuration settings can adversely impact system's availability, performance, and correctness. We refer to these wrong configuration settings as configuration bugs. The importance of configuration bugs has prompted many researchers to study it, and past studies can be grouped into three categories: detection, localization, and fixing of configuration bugs. In the work, we focus on the detection of configuration bugs, in particular, we follow the line-of-work that tries to predict if a bug report is caused by a wrong configuration setting. Automatically prediction of whether a bug is a configuration bug can help developers reduce debugging effort. We propose a novel approach named EFSPredictor which applies ensemble feature selection on the natural-language description of a bug report. It uses different feature selection approaches (e.g., ChiSquare, GainRatio and Relief) which output different ranked lists of textual features. Next, to obtain a set of representative textual features, EFSPredictor first assigns different scores to the features outputted by these feature selection approaches. Next, for each feature, EFSPredictor sums up the scores outputted by the multiple ranked lists, and outputs the top features (e.g., 25% of the total number of features) as the selected features. Finally, EFSPredictor builds a prediction model based on the selected features. We conduct experiments on 5 bug report datasets (i.e., accumulo, activemq, camel, flume, and wicket) containing a total of 3,203 bugs. The experiment results show that, on average across the 5 projects, EFSPredictor achieves an F1-score to 0.57, which improves the state-of-the-art approach proposed by Xia et al. by 14%.

Keywords

Configuration Bugs, Data Mining, Ensemble Feature Selection

Discipline

Databases and Information Systems | Data Storage Systems

Research Areas

Software and Cyber-Physical Systems

Publication

22nd Asia-Pacific Software Engineering Conference: APSEC 2015, New Delhi, India, 2015 December 1-4

First Page

206

Last Page

213

ISBN

9781467396448

Identifier

10.1109/APSEC.2015.38

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/APSEC.2015.38

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