バグ レポート ブンルイ ト ケッカン ヨソク ニ オケル ジドウカ シエン

博第1374号甲第1374号博士(工学)奈良先端科学技術大学院大

Comparing hierarchical dirichlet process with latent dirichlet allocation in bug report multiclass classification

SNPD 2014 : 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 30 June-2 July 2014, Las Vegas, NV, USABug reports play essential roles in many software engineering tasks. Since validity and performance of these tasks definitely rely on the quality of bug reports, accurate information from bug reports is very important. However, as found in previous study, significant numbers of reports classified as bug are not really a bug. Recent studies proposed techniques to automatically classify bug reports into binary classes, yet there is still more to desire. These bug reports can be classified into multiple classes, which could help to identify what these reports are actually about. Moreover, previous study only looks into one possibility of topic modeling, that is, Latent Dirichlet Allocation (LDA). While LDA has its advantage, parameter tuning is required. In this paper, we propose a nonparametric approach to automatically classify bug reports with, another topic modeling method, Hierarchical Dirichlet Process (HDP). The result indicates that our nonparametric approach performance is comparable to the parametric one. We also examine various aspects of LDA to provide more thoroughly understanding of this process

Automatic Unsupervised Bug Report Categorization

2014 6th International Workshop on Empirical Software Engineering in Practice, 12-13 Nov. 2014, Osaka, JapanBackground: Information in bug reports is implicit and therefore difficult to comprehend. To extract its meaning, some processes are required. Categorizing bug reports is a technique that can help in this regard. It can be used to help in the bug reports management or to understand the underlying structure of the desired project. However, most researches in this area are focusing on a supervised learning approach that still requires a lot of human afford to prepare a training data. Aims: Our aim is to develop an automated framework than can categorize bug reports, according to their hidden characteristics and structures, without the needed for training data. Method: We solve this problem using clustering, unsupervised learning approach. It can automatically group bug reports together based on their textual similarity. We also propose a novel method to label each group with meaningful and representative names. Results: Experiment results show that our framework can achieve performance comparable to the supervised learning approaches. We also show that our labeling process can label each cluster with representative names according to its characteristic. Conclusion: Our framework could be used as an automated categorization system that can be applied without prior knowledge or as an automated labeling suggestion system