En-LDA: An Novel Approach to Automatic Bug Report Assignment with Entropy Optimized Latent Dirichlet Allocation

We propose an approach called Bug report assignment with topic modeling and heterogeneous network analysis (BAHA) to automatically assign bug reports to developers. Existing studies adopt social network analysis to characterize the collaboration of developers. The networks used in these studies are all homogenous. In real practice of bug resolution, different developers collaborate on different bug reports that makes the homogenous network unable to capture this information. We use heterogeneous network to describe the relations between reporters, bug reports and developers to characterize developers' collaboration. Experiments on Eclipse JDT project show that BAHA outperforms the state of art methods on automatic bug report assignment.

Bug handling is an essential part of the software development process. Ideally, in a bug‐tracking system, bugs are reported, fixed, verified, and closed. In some cases, bugs have to be reopened mostly due to an incorrect fix. However, instead of reopening the existing bug report, users may submit a new report for a previously reported bug, which results in duplicate bug reports. Users might report duplicate bugs if they miss the previous bug report or if the previous bug is unresolved (i.e., in progress) and therefore cannot be reopened. These duplicate bug reports may cost extra maintenance efforts in triaging and bug fixing. There have been several studies on characterizing reopened bugs and duplicate bug reports; however, to the best of our knowledge, there has been no prior work on understanding the dynamics of their interaction, which ismissed reopenbugs. Our study is based on analyzing the difference between duplicate and nonduplicate bugs and further categorizing the duplicate bugs. In this regard, we categorize duplicate bugs according to the original bug's resolution status at the duplicate's creation time as Master‐Unresolved bugs and Master‐Resolved (Missed Reopen) bugs to distinguish their properties. We compare these two types of bugs in terms of their relationship to their master bug, bug surface time, bug fix time, bug's severity, and the number of users involved. We perform case studies using the Eclipse and Mozilla projects' bug repositories that include more than 165,500 and 394,000 bug reports, respectively. Additionally, we investigate the perceived importance, impact, and causes of duplicate bugs, as well as the difference between nonduplicate and duplicate bugs and its categories for practitioners in the software industry by conducting a survey.

FineLocator: A novel approach to method-level fine-grained bug localization by query expansion

As the scale and complexity of software products increase, software maintenance on bug resolution has become a challenging work.In the process of software implementation, developers often use bug reports, source code and change history to help solve bugs.However, hundreds of bug reports are being submitted every day.It is time-consuming and effortless for developers to review all the bug reports.To facilitate the assignment of bug reports, existing developer recommendation systems typically recommend the developer who has the fullest potential.However, bug reports are highly varied; time that the developers may spend fixing them is also important.To address the problem of developer recommendation, we propose a developer recommendation system with awareness of accuracy and cost (DRAC).This recommendation system is based on modern portfolio theory by striking a balance between accuracy and cost (time).We evaluate our approach with experiments on data collected from Bugzilla 1 .

Open-source development projects typically support an open bug repository to which both developers and users can report bugs. A report that appears in this repository must be triaged to determine if the report is one which requires attention and if it is, which developer will be assigned the responsibility of resolving the report. Large open-source developments are burdened by the rate at which new bug reports appear in the bug repository. The thesis of this work is that the task of triage can be eased by using a semi-automated approach to assign bug reports to developers. The approach consists of constructing a recommender for bug assignments; examined are both a range of algorithms that can be used and the various kinds of information provided to the algorithms. The proposed work seeks to determine through human experimentation a sufficient level of precision for the recommendations, and to analytically determine the trade-offs of the various algorithmic and information choices.

In this paper, we present a new approach to localize a bug in the software source file hierarchy. The proposed approach uses log files of the revision control system and bug reports information in open bug repository of open source projects to train a Support Vector Machine (SVM) classifier. Our approach employs textual information in summary and description of bugs reported to the bug repository, in order to form machine learning features. The class labels are revision paths of fixed issues, as recorded in the log file of the revision control system. Given an unseen bug instance, the trained classifier can predict which part of the software source file hierarchy (revision path) is more likely to be related to this issue. Experimental results on more than 2000 bug reports of ‘UI’component of the Eclipse JDT project from the initiation date of the project until November 24, 2009 (about 8 years) using this approach, show weighted precision and recall values of about 98% on average.

Many software development projects use work items such as tasks or bug reports to describe the work to be done. Some projects allow end-users or clients to enter new work items. New work items have to be triaged. The most important step is to assign new work items to a responsible developer. There are existing approaches to automatically assign bug reports based on the experience of certain developers based on machine learning. We propose a novel model-based approach, which considers relations from work items to the system specification for the assignment. We compare this new approach to existing techniques mining textual content as well as structural information. All techniques are applied to different types of work items, including bug reports and tasks. For our evaluation, we mine the model repository of three different projects. We also included history data to determine how well they work in different states.

Bug triage is the process of quickly assigning bug reports to the most suitable developer so that the defect can be fixed quickly. Usually, the manual triage method not only consumes a lot of time for developers, but also has low ac-curacy. Therefore, how to automatically and efficiently allocate defects has become a hot research problem in the field of software maintenance. At present, the automatic bug triage method has become the mainstream technology, but there are also problems such as feature extraction and insufficient text representation ability. In view of the above problems, the task of automatic bug triage is regarded as a text classification problem. The method for automatic bug triage of multi-scale convolutional neural networks based on word vectors MCNN- BT can more effectively capture the important text features of bug reports, thereby providing more accurate defect repairer recommendation services. Empirical research is carried out in five software projects, Platform, Pde, Jdt, Cdt, Birt. The experimental results show that the prediction accuracy of MCNN-BT method is higher than that of the Naive Bayes method and the Latent Dirichlet Allocation(LDA) method.

For large open source projects (e.g., Eclipse, Mozilla), developers usually utilize bug reports to facilitate software maintenance tasks such as fixer assignment. However, there are a large portion of short reports in bug repositories. We find that 78.1% of bug reports only include less than 100 words in Eclipse and require bug fixers to spend more time on resolving them due to limited informative contents. To address this problem, in this paper, we propose a novel approach to enrich bug reports. Concretely, we design a sentence ranking algorithm based on a new textual similarity metric to select the proper contents for bug report enrichment. For the enriched bug reports, we conduct a user study to assess whether the additional sentences can provide further help to fixer assignment. Moreover, we assess whether the enriched versions can improve the performance of automated fixer recommendation. In particular, we perform three popular automated fixer recommendation approaches on the enriched bug reports of Eclipse, Mozilla, and GNU Compiler Collection (GCC). The experimental results show that enriched bug reports improve the average F-measure scores of the automated fixer recommendation approaches by up to 10% for DREX, 13.37% for DRETOM, and 8% for DevRec when top-10 bug fixers are recommended.

To collect software bugs found by users, development teams often set up bug trackers using systems such as Bugzilla. Developers would then fix some of the bugs and commit corresponding code changes into version control systems such as svn or git. Unfortunately, the links between bug reports and code changes are missing for many software projects as the bug tracking and version control systems are often maintained separately. Yet, linking bug reports to fix commits is important as it could shed light into the nature of bug fixing processes and expose patterns in software management. Bug linking solutions, such as ReLink, have been proposed. The demonstration of their effectiveness however faces a number of issues, including a reliability issue with their ground truth datasets as well as the extent of their measurements. We propose in this study a benchmark for evaluating bug linking solutions. This benchmark includes a dataset of about 12,000 bug links from 10 programs. These true links between bug reports and their fixes have been provided during bug fixing processes. We designed a number of research questions, to assess both quantitatively and qualitatively the effectiveness of a bug linking tool. Finally, we apply this benchmark on ReLink to report the strengths and limitations of this bug linking tool.

Latent Dirichlet Allocation (LDA), as the most popular topic model, models documents as mixtures of topics and topics as mixtures of words. Topic mixture well represents documents while words mixture extracts meaningful topics from a corpus. The nature of LDA makes it a powerful tool in documents organizing and corpus summarizing. One limitation of LDA is that its performance depends heavily on the priors. Researchers show priors matters in LDA and propose methods to learn the priors for better modeling, regardless of using symmetric priors. However, LDA modeling ability does not necessarily consent with the performance of LDA in documents representation and topic extraction. In this paper, we propose a novel prior setting for LDA. The setting improves LDA in both documents representation and topic extraction performance. We experiment to compare our setting with symmetric priors and previously proposed priors that enhances modeling ability. Experiments on the topic quality show that LDA with our prior setting extracts better topics than LDA with other kinds of prior settings. We compare LDA document representation ability through tasks such as document clustering and document classification. These experiments demonstrate LDA with our proposed priors represents document better. Moreover, our analyses also reveal that better modeling does not necessarily lead to better performance in documents representation and topic extraction.

Latent Dirichlet allocation (LDA) is a statistical model that is often used to discover topics or themes in a large collection of documents. In the LDA model, topics are modeled as discrete distributions over a finite vocabulary of words. The LDA is also a popular choice to model other datasets spanning a discrete domain, such as population genetics and social networks. However, in order to model data spanning a continuous domain with the LDA, discrete approximations of the data need to be made. These discrete approximations to continuous data can lead to loss of information and may not represent the true structure of the underlying data. We present an augmented version of the LDA topic model, where topics are represented using Gaussian mixture models (GMMs), which are multi-modal distributions spanning a continuous domain. This augmentation of the LDA topic model with Gaussian mixture topics is denoted by the GMM-LDA model. We use Gibbs sampling to infer model parameters. We demonstrate the utility of the GMM-LDA model by applying it to the problem of clustering sleep states in electroencephalography (EEG) data. Results are presented demonstrating superior clustering performance with our GMM-LDA algorithm compared to the standard LDA and other clustering algorithms.

A significant amount of time is spent by software developers in investigating bug reports. It is useful to indicate when a bug report will be closed, since it would help software teams to prioritise their work. Several studies have been conducted to address this problem in the past decade. Most of these studies have used the frequency of occurrence of certain developer activities as input attributes in building their prediction models. However, these approaches tend to ignore the temporal nature of the occurrence of these activities. In this paper, a novel approach using Hidden Markov Models and temporal sequences of developer activities is proposed. The approach is empirically demonstrated in a case study using eight years of bug reports collected from the Firefox project. Our proposed model correctly identifies bug reports with expected bug fix times. We also compared our proposed approach with the state of the art technique in the literature in the context of our case study. Our approach results in approximately 33 percent higher F-measure than the contemporary technique based on the Firefox project data.

With the increasing number of software bugs, bug fixing plays an important role in software development and maintenance. To improve the efficiency of bug resolution, developers utilize bug reports to resolve given bugs. Especially, bug triagers usually depend on bugs' descriptions to suggest priority levels for reported bugs. However, manual priority assignment is a time-consuming and cumbersome task. To resolve this problem, recent studies have proposed many approaches to automatically predict the priority levels for the reported bugs. Unfortunately, these approaches still face two challenges that include words' nonconsecutive semantics in bug reports and the imbalanced data. In this article, we propose a novel approach that graph convolutional networks (GCN) based on weighted loss function to perform the priority prediction for bug reports. For the first challenge, we build a heterogeneous text graph for bug reports and apply GCN to extract words' semantics in bug reports. For the second challenge, we construct a weighted loss function in the training phase. We conduct the priority prediction on four open-source projects, including Mozilla, Eclipse, Netbeans, and GNU compiler collection. Experimental results show that our method outperforms two baseline approaches in terms of the F-measure by weighted average of 13.22%.

Bug reports are widely employed to facilitate software tasks in software maintenance. Since bug reports are contributed by people, the authorship characteristics of contributors may heavily impact the performance of resolving software tasks. Poorly written bug reports may delay developers when fixing bugs. However, no in-depth investigation has been conducted over the authorship characteristics. In this study, we first leverage byte-level $N$-grams to model the authorship characteristics and employ Normalized Simplified Profile Intersection (NSPI) to identify the similarity of the authorship characteristics. Then, we investigate a series of properties related to contributors authorship characteristics, including the evolvement over time and the variation among distinct products in open source projects. Moreover, we show how to leverage the authorship characteristics to facilitate a well-known task in software maintenance, namely Bug Report Summarization (BRS). Experiments on open source projects validate that incorporating the authorship characteristics can effectively improve a state-of-the-art method in BRS. Our findings suggest that contributors should retain stable authorship characteristics and the authorship characteristics can assist in resolving software tasks.