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

Accurate Developer Recommendation for Bug Resolution

NSF

A framework for active software engineering ontology

The passive structure of ontologies results in the ineffectiveness to access and manage the knowledge captured in them. This research has developed a framework for active Software Engineering Ontology based on a multi-agent system. It assists software development teams to effectively access, manage and share software engineering knowledge as well as project information to enable effective and efficient communication and coordination among teams. The framework has been evaluated through the prototype system as proof-of-concept experiments

Retrieving curated Stack Overflow Posts of similar project tasks

Software development depends on diverse technologies and methods and as a result, software development teams often handle issues in which team members are not experts. In order to address this lack of expertise, developers typically search for information on web-based Q&A sites such as Stack Overflow, a well-known place to find solutions to specific technology-related problems. Access to these web-based Q&A locations is currently not integrated into the software development environment, and since the associations between software development projects and the supporting sources of known solutions, usually referred to as knowledge, is not explicitly recorded, software developers often need to search for solutions to similar recurring issues multiple times. This lack of integration hinders the reuse of the knowledge obtained, besides not avoiding efforts of search and selection, curation, of this knowledge over and over again. This research aims at proposing a study regarding explicitly associating project elements (such as project tasks) to Stack Overflow posts that have already been curated by developers, and presents a study about Stack Overflow posts suggestions to developers based on similarity of project tasks.O desenvolvimento de software depende de diversas tecnologias e métodos e, como resultado, as equipes de desenvolvimento de software geralmente lidam com problemas em que não são especialistas. Para lidar com a falta de conhecimento, desenvolvedores normalmente procuram informações em sites de perguntas e respostas, como o Stack Overflow, um site usado para encontrar soluções para problemas específicos relacionados à tecnologia. O acesso a esses sites não é integrado ao ambiente de desenvolvimento de software e porque as associações entre os projetos de desenvolvimento de software e as fontes de suporte de soluções conhecidas não são explicitamente registradas. Com isso, desenvolvedores de software podem investir um esforço em procurar soluções para problemas semelhantes várias vezes. Essa falta de integração dificulta o reuso do conhecimento obtido, além de não evitar esforços de busca e seleção, a curadoria, repetidas vezes. Esta pesquisa tem como objetivo realizar um estudo sobre a associação explicita entre elementos do projeto (como tarefas de projeto) a publicações do Stack Overflow que já sofreram curadoria por desenvolvedores, e apresenta um estudo sobre sugestões de publicações do Stack Overflow a desenvolvedores com base na similaridade de tarefas de projeto

Bug Triaging with High Confidence Predictions

Correctly assigning bugs to the right developer or team, i.e., bug triaging, is a costly activity. A concerted effort at Ericsson has been done to adopt automated bug triaging to reduce development costs. We also perform a case study on Eclipse bug reports. In this work, we replicate the research approaches that have been widely used in the literature including FixerCache. We apply them on over 10k bug reports for 9 large products at Ericsson and 2 large Eclipse products containing 21 components. We find that a logistic regression classifier including simple textual and categorical attributes of the bug reports has the highest accuracy of 79.00% and 46% on Ericsson and Eclipse bug reports respectively. Ericsson’s bug reports often contain logs that have crash dumps and alarms. We add this information to the bug triage models. We find that this information does not improve the accuracy of bug triaging in Ericsson’s context. Eclipse bug reports contain the stack traces that we add to the bug triaging model. Stack traces are only present in 8% of bug reports and do not improve the triage accuracy. Although our models perform as well as the best ones reported in the literature, a criticism of bug triaging at Ericsson is that accuracy is not sufficient for regular use. We develop a novel approach that only triages bugs when the model has high confidence in the triage prediction. We find that we improve the accuracy to 90% at Ericsson and 70% at Eclipse, but we can make predictions for 62% and 25% of the total Ericsson and Eclipse bug reports,respectively

Including Everyone, Everywhere:Understanding Opportunities and Challenges of Geographic Gender-Inclusion in OSS

The gender gap is a significant concern facing the software industry as the development becomes more geographically distributed. Widely shared reports indicate that gender differences may be specific to each region. However, how complete can these reports be with little to no research reflective of the Open Source Software (OSS) process and communities software is now commonly developed in? Our study presents a multi-region geographical analysis of gender inclusion on GitHub. This mixed-methods approach includes quantitatively investigating differences in gender inclusion in projects across geographic regions and investigate these trends over time using data from contributions to 21,456 project repositories. We also qualitatively understand the unique experiences of developers contributing to these projects through a survey that is strategically targeted to developers in various regions worldwide. Our findings indicate that gender diversity is low across all parts of the world, with no substantial difference across regions. However, there has been statistically significant improvement in diversity worldwide since 2014, with certain regions such as Africa improving at faster pace. We also find that most motivations and barriers to contributions (e.g., lack of resources to contribute and poor working environment) were shared across regions, however, some insightful differences, such as how to make projects more inclusive, did arise. From these findings, we derive and present implications for tools that can foster inclusion in open source software communities and empower contributions from everyone, everywhere

Intelligent Software Bugs Localization, Triage and Prioritization

One of the time-consuming software maintenance tasks is the localization of software bugs especially in large systems. Developers have to follow a tedious process to reproduce the abnormal behavior then inspect a large number of files in order to resolve the bugs. Furthermore, software developers are usually overwhelmed with several reports of critical bugs to be addressed urgently and simultaneously. The management of these bugs is a complex problem due to the limited resources and the deadlines-pressure. Another critical task in this process is to assign appropriate priority to the bugs and eventually assign them to the right developers for resolution. Several studies have been proposed for bugs localization, the majority of them are recommending classes as outputs which may still require high inspection effort. In addition, there is a significant difference between the natural language used in bug reports and the programming language which limits the efficiency of existing approaches since most of them are mainly based on lexical similarity. Most of the existing studies treated bug reports in isolation when assigning them to developers. They also lack the understanding of dynamics of changing bug priorities. Thus, developers may spend considerable cognitive efforts moving between completely unrelated bug reports. To address these challenges, we proposed the following research contributions: 1. We proposed an automated approach to find and rank the potential classes and methods in order to localize software defects. Our approach finds a good balance between minimizing the number of recommended classes and maximizing the relevance of the proposed solution using a hybrid multi-objective optimization algorithm combining local and global search. Our hybrid multi-objective approach is able to successfully locate the true buggy methods within the top 10 recommendations for over 78% of the bug reports leading to a significant reduction of developers' effort comparing to class-level bug localization techniques. 2. We proposed an automated bugs triage approach based on the dependencies between several open bug reports. We defined the dependency between two bug reports as the number of common files to be inspected to localize the bugs. Then, we adopted multi-objective search to rank the bug reports for programmers. The results show a significant time reduction of over 30% in localizing the bugs simultaneously comparing to the traditional bugs prioritization technique based on only priorities. 3. We performed an empirical study to observe and understand the changes in bugs' priority in order to build a 3-W model on Why and When bug priorities change, and Who performs the change. We conducted interviews and a survey with practitioners as well as performed a quantitative analysis large database of bugs reports. As a result, we observed frequent changes in bug priorities and their impact on delaying critical bug fixes especially before shipping a new release.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/170906/1/Rafi Almhana Final Dissertation.pdfDescription of Rafi Almhana Final Dissertation.pdf : Dissertatio