Researchers in software engineering have attempted to improve software development by mining and analyzing software repositories. Since the majority of the software engineering data is unstructured, researchers have applied Information Retrieval (IR) techniques to help software development. The recent advances of IR, especially statistical topic models, have helped make sense of unstructured data in software repositories even more. However, even though there are hundreds of studies on applying topic models to software repositories, there is no study that shows how the models are used in the software engineering research community, and which software engineering tasks are being supported through topic models. Moreover, since the performance of these topic models is directly related to the model parameters and usage, knowing how researchers use the topic models may also help future studies make optimal use of such models. Thus, we surveyed 167 articles from the software engineering literature that make use of topic models. We find that i) most studies centre around a limited number of software engineering tasks; ii) most studies use only basic topic models; iii) and researchers usually treat topic models as black boxes without fully exploring their underlying assumptions and parameter values. Our paper provides a starting point for new researchers who are interested in using topic models, and may help new researchers and practitioners determine how to best apply topic models to a particular software engineering task.

A survey on the use of topic models when mining software repositories

Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modeling algorithm named multi-feature topic model (MTM) which extends Latent Dirichlet Allocation (LDA) for bug triaging. MTM considers product and component information of bug reports to map the term space to the topic space. Finally, we propose an incremental learning method named TopicMiner which considers the topic distribution of a new bug report to assign an appropriate fixer based on the affinity of the fixer to the topics. We pair TopicMiner with MTM ( TopicMiner $^{MTM}$ ). We have evaluated our solution on 5 large bug report datasets including GCC, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 227,278 bug reports. We show that TopicMiner $^{MTM}$ can achieve top-1 and top-5 prediction accuracies of 0.4831-0.6868, and 0.7686-0.9084, respectively. We also compare TopicMiner $^{MTM}$ with Bugzie, LDA-KL, SVM-LDA, LDA-Activity, and Yang et al.'s approach. The results show that TopicMiner $^{MTM}$ on average improves top-1 and top-5 prediction accuracies of Bugzie by 128.48 and 53.22 percent, LDA-KL by 262.91 and 105.97 percent, SVM-LDA by 205.89 and 110.48 percent, LDA-Activity by 377.60 and 176.32 percent, and Yang et al.'s approach by 59.88 and 13.70 percent, respectively.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4693&context=sis_research

Improving Automated Bug Triaging with Specialized Topic Model

StackOverflow provides a popular platform where developers post and answer questions. Recently, Treude et al. manually label 385 questions in StackOverflow and group them into 10 categories based on their contents. They also analyze how tags are used in StackOverflow. In this study, we extend their work to obtain a deeper understanding on how developers interact with one another on such a question and answer web site. First, we analyze the distributions of developers who ask and answer questions. We also investigate if there is a segregation of the StackOverflow community into questioners and answerers. We also perform automated text mining to find the various kinds of topics asked by developers. We use Latent Dirichlet Allocation (LDA), a well known topic modeling approach, to analyze the contents of tens of thousands of questions and answers, and produce five topics. Our topic modeling strategy provides an alternative perspective different from that of Treude et al. for categorizing StackOverflow questions. Each question can now be categorized into several topics with different probabilities, and the learned topic model could automatically assign a new question to several categories with varying probabilities. Last but not least, we show the distributions of questions and developers belonging to various topics generated by LDA.

/pdf/an-empirical-study-on-developer-interactions-in-15g5h0r301.pdf

An empirical study on developer interactions in StackOverflow

Twitter is one of the most popular social networks. Previous research found that users employ Twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution. However, due to their large number---in the range of thousands per day for popular applications---a manual analysis is unfeasible.In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets. We ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement. Our results show that ALERTme is an effective approach for filtering, summarizing and ranking tweets about software applications. ALERTme enables the exploitation of Twitter as a feedback channel for information relevant to software evolution, including end-user requirements.

/pdf/a-little-bird-told-me-mining-tweets-for-requirements-and-2hh3xetg7s.pdf

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

One question which frequently arises within the context of artifacts stored in a bug tracking repository is: “who should work on this bug report?” A number of approaches exist to semi-automatically identify and recommend developers, e.g. using machine learning techniques and social networking analysis. In this work, we propose a new approach for assignee recommendation leveraging user activities in a bug tracking repository. Within the bug tracking repository, an activity profile is created for each user from the history of all his activities (i.e. review, assign, and resolve). This profile, to some extent, indicates the user's role, expertise, and involvement in this project. These activities influence and contribute to the identification and ranking of suitable assignees. In order to evaluate our work, we apply it to bug reports of three different projects. Our results indicate that the proposed approach is able to achieve an average hit ratio of 88%. Comparing this result to the LDA-SVM - based assignee recommendation technique, it was found that the proposed approach performs better.

Bug report assignee recommendation using activity profiles

Software developers, particularly in open-source projects, rely on bug repositories to organize their work. On a bug report, the component field is used to indicate to which team of developers a bug should be routed. Researchers have shown that incorrect categorization of newly received bug reports to components can cause potential delays in the resolution of bug reports. Approaches have been developed that consider the use of machine learning approaches, specifically Support Vector Machines (svm), to automatically categorize bug reports into the appropriate component to help streamline the process of solving a bug. One drawback of an SVM-based approach is that the results of categorization can be uneven across various components in the system if some components receive less reports than others. In this paper, we consider broadening the consistency of the recommendations produced by an automatic approach by investigating three approaches to automating bug report categorization: an approach similar to previous ones based on an SVM classifier and Term Frequency Inverse Document Frequency(svm-tf-idf), an approach using Latent Dirichlet Allocation (LDA) with SVM (svm-lda) and an approach using LDA and Kullback Leibler divergence (lda-kl). We found that lda-kl produced recalls similar to those found previously but with better consistency across all components for which bugs must be categorized.

Kalyanasundaram Somasundaram

Papers

Automatic categorization of bug reports using latent Dirichlet allocation