Automated Configuration Bug Report Prediction Using Text Mining

2014 19th International Conference on Engineering of Complex Computer Systems

Lo²,

Wang³

et al. 2014

Self Cite

Abstract-Due to the complexity of software systems, defects are inevitable. Understanding the types of defects could help developers to adopt measures in current and future software releases. In practice, developers often categorize defects into various types. One common categorization is based on fault triggers of defects. Fault trigger is a set of conditions which activate a defect (i.e., fault) and propagate the defect into a failure. In general, there are two types of defect based fault triggering conditions, Bohrbug and Mandelbug. Bohrbug refers to a bug which can be easily isolated, and its activation and error propagation is simple. Mandelbug refers to a bug whose activation and/or error propagation is complex (e.g., a time lag between the fault activation and the failure occurrence). With these category labels, developers can better perform post-mortem analysis to identify common characteristic of the defects, and design specific fault-tolerance mechanisms.However, in most software systems, these category labels are often unavailable. To address this problem, in this paper, we propose a text mining solution which categorize defects into fault trigger categories by analyzing the natural-language description of bug reports. A previous study shows that Mandelbug is more complex and needs more time to be fixed. Thus, to better identify Mandelbugs, we propose a novel fUzzy Set based fEature Selection algorithm named USES, which selects the features (i.e., terms) which have high ability to distinguish Mandelbugs from Bohrbugs. USES first caches a set of terms based on their fuzzy affinity scores to Bohrbug or Mandelbug. Next, it iterates many times, and in each iteration, it selects a subset of terms, and builds a classifier on these terms. USES selects the classifier and the terms which could achieve the best performance on a training data. We evaluate our solution on 4 datasets including Linux, Mysql, Apache HTTPD, and AXIS containing a total of 809 bug reports. We show that USES with naive Bayes multinomial achieves the best performance; it achieves Mandelbug F-measure scores of 0.298 -0.615. We also compare USES with other baseline approaches. The results show that USES on average improves Mandelbug F-measure scores of the best performing baseline by 12.3%.

Section: A Characterization and Prediction Of Bugsmentioning

confidence: 87%

Automatic Defect Categorization Based on Fault Triggering Conditions

2014 19th International Conference on Engineering of Complex Computer Systems

Lo²,

Wang³

et al. 2014

Self Cite

“…Huang et al propose AutoODC which leverages a text classification technique to categorize defects according to their impact [52]. Xia et al propose the usage of text mining techniques to identify configuration bugs [53].…”

Section: B Bug Report Managementmentioning

confidence: 99%

An Empirical Study of Bug Fixing Rate

Zou

2015 IEEE 39th Annual Computer Software and Applications Conference

Zhang

et al. 2015

Self Cite

Abstract-Bug fixing is one of the most important activities in software development and maintenance. A software project often employs an issue tracking system such as Bugzilla to store and manage their bugs. In the issue tracking system, many bugs are invalid but take unnecessary efforts to identify them. In this paper, we mainly focus on bug fixing rate, i.e., the proportion of the fixed bugs in the reported closed bugs. In particular, we study the characteristics of bug fixing rate and investigate the impact of a reporter's different contribution behaviors to the bug fixing rate. We perform an empirical study on all reported bugs of two large open source software communities Eclipse and Mozilla. We find (1) the bug fixing rates of both projects are not high; (2) there exhibits a negative correlation between a reporter's bug fixing rate and the average time cost to close the bugs he/she reports; (3) the amount of bugs a reporter ever fixed has a strong positive impact on his/her bug fixing rate; (4) reporters' bug fixing rates have no big difference, whether their contribution behaviors concentrate on a few products or across many products; (5) reporters' bug fixing rates tend to increase as time goes on, i.e., developers become more experienced at reporting bugs.

“…For those that are configuration bugs, developers can focus their effort on checking configuration files rather than source code. Two most recent works in this direction are by Arshad et al [8] and Xia et al [38]. Xia We present a new approach named EFSPredictor which combines multiple feature selection technologies to obtain a set of representative features, and then builds a statistical prediction model on these representative features extracted from historical bug reports with known labels (i.e., nonconfiguration or configuration).…”

Section: Introductionmentioning

confidence: 99%

EFSPredictor: Predicting Configuration Bugs with Ensemble Feature Selection

2015 Asia-Pacific Software Engineering Conference (APSEC)

et al. 2015

Self Cite

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%.