Semantic Jira - Semantic Expert Finder in the Bug Tracking Tool Jira

The semantic expert recommender extension for the Jira bug tracking system semantically searches for similar tickets in Jira and recommends experts and links to existing organizational (Wiki) knowledge for each ticket. This helps to avoid redundant work and supports the search and collaboration with experts in the project management and maintenance phase based on semantically enriched tickets in Jira.Comment: published in proceedings of the 9th International Workshop on Semantic Web Enabled Software Engineering (SWESE2013), Berlin, Germany, December 2-5, 201

PREM: Prestige Network Enhanced Developer-Task Matching for Crowdsourced Software Development

Many software organizations are turning to employ crowdsourcing to augment their software production. For current practice of crowdsourcing, it is common to see a mass number of tasks posted on software crowdsourcing platforms, with little guidance for task selection. Considering that crowd developers may vary greatly in expertise, inappropriate developer-task matching will harm the quality of the deliverables. It is also not time-efficient for developers to discover their most appropriate tasks from vast open call requests. We propose an approach called PREM, aiming to appropriately match between developers and tasks. PREM automatically learns from the developers’ historical task data. In addition to task preference, PREM considers the competition nature of crowdsourcing by constructing developers’ prestige network. This differs our approach from previous developer recommendation methods that are based on task and/or individual features. Experiments are conducted on 3 TopCoder datasets with 9,191 tasks in total. Our experimental results show that reasonable accuracies are achievable (63%, 46%, 36% for the 3 datasets respectively, when matching 5 developers to each task) and the constructed prestige network can help improve the matching results

Effective Bug Triage based on Historical Bug-Fix Information

International audienceFor complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this pa-per, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called BugFixer, which recommends developers for a new bug report based on historical bug-fix in-formation. BugFixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship be-tween the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, BugFixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate BugFixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects

Candoia: A Platform and an Ecosystem for Building and Deploying Versatile Mining Software Repositories Tools

Research on mining software repositories (MSR) has shown great promise during the last decade in solving many challenging software engineering problems. There exists, however, a ‘valley of death’ between these significant innovations in the MSR research and their deployment in practice. The significant cost of converting a prototype to software; need to provide support for a wide variety of tools and technologies e.g. CVS, SVN, Git, Bugzilla, Jira, Issues, etc, to improve applicability; and the high cost of customizing tools to practitioner-specific settings are some key hurdles in transition to practice. We describe Candoia, a platform and an ecosystem that is aimed at bridging this valley of death between innovations in MSR research and their deployment in practice. We have implemented Candoia and provide facilities to build and publish MSR ideas as Candoia apps. Our evaluation demonstrates that Candoia drastically reduces the cost of converting an idea to an app, thus reducing the barrier to transitioning research findings into practice. We also see versatility, in Candoia app’s ability to work with a variety of tools and technologies that the platform supports. Finally, we find that customizing Candoia app to fit project-specific needs is often well within the grasp of developers

Bug Fix Time Optimization Using Matrix Factorization and Iterative Gale-Shaply Algorithms

Bug triage is an essential task in software maintenance phase. It assigns developers (fixers) to bug reports to fix them. This process is performed manually by a triager, who analyzes developers profiles and submitted bug reports to make suitable assignments. Bug triaging process is time consuming thus automating this process is essential to improve the quality of software. Previous work addressed triaging problem either as an information retrieval or classification problem. This paper tackles this problem as a resource allocation problem, that aims at the best assignments of developers to bug reports, that reduces the total fixing time of the newly submitted bug reports, in addition to the even distribution of bug reports over developers. In this paper, a combination of matrix factorization and Gale Shapely algorithm, supported by the differential evolution is firstly introduced to optimize the total fix time and normalize developers work load. Matrix factorization is used to establish a recommendation system for Gale-Shapley to make assignment decisions. Differential evolution provides the best set of weights to build developers score profiles. The proposed approach is assessed over three repositories, Linux, Apache and Eclipse. Experimental results show that the proposed approach reduces the bug fixing time, in comparison to the manual triage, by 80.67%, 23.61% and 60.22% over Linux, Eclipse and Apache respectively. Moreover, the workload for the developers is uniform.Comment: 14 page, 7 figures, 8 tables, 10 equation

HISTORICAL ANALYSIS OF MESSAGE CONTENTS TO RECOMMEND ISSUES TO OPEN SOURCE SOFTWARE CONTRIBUTORS

Developers of distributed open source projects make use of issue tracker tools to coordinate their work. These tools store valuable information, maintaining a log of relevant decisions and bug solutions. Finding the appropriate issues to contribute can be hard, as the high volume of data increases contributors’ overhead. This paper shows the importance of the content of issue tracker discussions in an open source project to build a classifier to predict the participation of a contributor in an issue. To design this prediction model, we used two machine learning algorithms called Naïve Bayes and J48. We used data from the Apache Hadoop Commons project to evaluate the use of the algorithms. By applying machine learning algorithms to the ten most active contributors of this project, we achieved an average recall of 66.82% for Naïve Bayes and 53.02% using J48. We achieved 64.31% of precision and 90.27% of accuracy using J48. We also conducted an exploratory study with five contributors that took part in fewer issues and achieved 77.41% of precision, 48% of recall, and 98.84% accuracy using J48 algorithm. The results indicate that the content of comments in issues of open source projects is a relevant factor to recommend issues to contributors.Os desenvolvedores de projetos de software livre distribuídos utilizam ferramentas de acompanhamento de pendências para coordenar o seu trabalho. Essas ferramentas armazenam informações importantes, mantendo registro de decisões importantes e soluções para bugs. Decidir sobre que pendências são as mais adequadas para se contribuir pode ser difícil, uma vez que a elevada quantidade de dados aumenta a pressão sobre os desenvolvedores. Este artigo mostra a importância do conteúdo das discussões que ocorrem por meio da ferramenta de acompanhamento de pendências em um projeto de software livre para a construção de um classificador para predizer a participação de um colaborador na solução de um problema. Para projetar este modelo de predição, utilizamos dois algoritmos de aprendizagem de máquina: Naïve Bayes e J48. Utilizamos dados do projeto Apache Hadoop Commons para avaliar o uso dos algoritmos. Aplicando algoritmos de aprendizado de máquina aos dez desenvolvedores mais ativos no projeto, obtivemos uma média de recall de 66,82% para Naïve Bayes e 53,02% usando J48. Obtivemos 64,31% de precisão e 90,27% de acurácia usando o J48. Também realizamos um estudo exploratório com cinco desenvolvedores que participaram na solução de um volume menor de problemas , obtendo 77,41% de precisão, 48% de recall, e 98,84% de acurácia usando o algoritmo J48. Os resultados indicam que o conteúdo dos comentários em pendências/ problemas em projetos de software livre representam um fator relevante com base no qual recomendar pendências aos desenvolvedores que colaboram com o projeto

Fuzzy set and cache-based approach for bug triaging

Software bugs are inevitable and bug fixing is an essential and costly phase during software development. Such defects are often reported in bug reports which are stored in an issue tracking system, or bug repository. Such reports need to be assigned to the most appropriate developers who will eventually fix the issue/bug reported. This process is often called Bug Triaging. Manual bug triaging is a difficult, expensive, and lengthy process, since it needs the bug triager to manually read, analyze, and assign bug fixers for each newly reported bug. Triagers can become overwhelmed by the number of reports added to the repository. Time and efforts spent into triaging typically diverts valuable resources away from the improvement of the product to the managing of the development process. To assist triagers and improve the bug triaging efficiency and reduce its cost, this thesis proposes Bugzie, a novel approach for automatic bug triaging based on fuzzy set and cachebased modeling of the bug-fixing capability of developers. Our evaluation results on seven large-scale subject systems show that Bugzie achieves significantly higher levels of efficiency and correctness than existing state-of-the-art approaches. In these subject projects, Bugzie\u27s accuracy for top-1 and top-5 recommendations is higher than those of the second best approach from 4-15% and 6-31%, respectively as Bugzie\u27s top-1 and top-5 recommendation accuracy is generally in the range of 31-51% and 70-83%, respectively. Importantly, existing approaches take from hours to days (even almost a month) to finish training as well as predicting, while in Bugzie, training time is from tens of minutes to an hour