Improving Developer Profiling and Ranking to Enhance Bug Report Assignment

Bug assignment plays a critical role in the bug fixing process. However, bug assignment can be a burden for projects receiving a large number of bug reports. If a bug is assigned to a developer who lacks sufficient expertise to appropriately address it, the software project can be adversely impacted in terms of quality, developer hours, and aggregate cost. An automated strategy that provides a list of developers ranked by suitability based on their development history and the development history of the project can help teams more quickly and more accurately identify the appropriate developer for a bug report, potentially resulting in an increase in productivity. To automate the process of assigning bug reports to the appropriate developer, several studies have employed an approach that combines natural language processing and information retrieval techniques to extract two categories of features: one targeting developers who have fixed similar bugs before and one targeting developers who have worked on source files similar to the description of the bug. As developers document their changes through their commit messages it represents another rich resource for profiling their expertise, as the language used in commit messages typically more closely matches the language used in bug reports. In this study, we have replicated the approach presented in [32] that applies a learning-to-rank technique to rank appropriate developers for each bug report. Additionally, we have extended the study by proposing an additional set of features to better profile a developer through their commit logs and through the API project descriptions referenced in their code changes. Furthermore, we explore the appropriateness of a joint recommendation approach employing a learning-to-rank technique and an ordinal regression technique. To evaluate our model, we have considered more than 10,000 bug reports with their appropriate assignees. The experimental results demonstrate the efficiency of our model in comparison with the state-of-the-art methods in recommending developers for open bug reports

Recommending Issue Reports to Developers Using Machine Learning

Tarkvarasüsteemide arendust viiakse tihti läbi iteratiivse protsessina ning erinevad tööüleasnded tekkivad siis kui leitakse defekte või tekib vajadus uue funktsionaalsuse järele. Need ülesanded salvestatakse probleemihalduse süsteemi, kust arendajad saavad sisendit oma tööle. Ülesannete jaotamine arendajatele võib toimude mitmel eri viisil. Üks populaarsemaid lähenemisi näeb ette, et arendajad valivad ise ülesandeid, mis neid huvitavad. Suurtes projektides võib see aga muutuda keeruliseks: ülesannete suure arvu tõttu on arendajatel raske aegsasti valida omale huvitav tööülesanne. Selle probleemi leevendamiseks esitatakse antud töös masinõppel põhinev soovitussüsteem, mis on võimeline probleemihalduse süsteemi ajaloost õppima milliseid ülesandeid on iga arendaja eelnevalt täitnud ja selle põhjal soovitada neile uusi ülesandeid. Süsteemi arendamiseks koguti 6 erinevast avatud lähtekoodiga projektist ülesandeid, kasutati erinevaid masinõppe meetodeid ja võrreldi tulemusi, et leida sobivaim. Soovitussüsteemi jõudluse hindamiseks kasutati täpsuse (precision), saagise (recall), f1-skoori (f1-score) ja keskmise täpsuse (mean average precision) mõõdikuid. Tulemused näitavad, et 100 tööülesande kirjelduse põhjal 10 igale arendajale sobivaima soovitamise puhul võib saavutada saagise 52.9% ja 96% vahel, mis on 6 kuni 9.5 korda parem 10 juhusliku töökirjelduse valimisest. Sarnased parandused saavutati ka teistes mõõdikutes.The development of a software system is often done through an iterative process and different change requests arise when bugs and defects are detected or new features need to be added. These requirements are recorded as issue reports and put in the backlog of the software project for developers to work on. The assignment of these issue reports to developers is done in different ways. One common approach is self-assignment, where the developers themselves pick the issue reports they are interested in and assign themselves. Practising self-assignment in large projects can be challenging for developers because the backlog of large projects become loaded with many issue reports, which makes it hard for developers to filter out the issue reports in line with their interest. To tackle this problem, a machine learning-based recommender system is proposed in this thesis. This recommender system can learn from the history of the issue reports that each developer worked on previously and recommend new issue reports suited to each developer. To implement this recommender system, issue reports were collected from 6 different opensource projects and different machine learning techniques were applied and compared in order to determine the most suitable one. For evaluating the performance of the recommender system, the Precision, Recall, F1-score and Mean Average Precision metrics were used. The results show that, from a backlog of 100 issue reports, by recommending the top 10 issue reports to each developer a recall ranging from 52.9% up to 96% can be achieved, which is 6 up to 9.5 times better than picking 10 issue reports randomly. Comparable improvements were also achieved in the other metrics

Recommending Bug Assignment Approaches for Individual Bug Reports: An Empirical Investigation

Multiple approaches have been proposed to automatically recommend potential developers who can address bug reports. These approaches are typically designed to work for any bug report submitted to any software project. However, we conjecture that these approaches may not work equally well for all the reports in a project. We conducted an empirical study to validate this conjecture, using three bug assignment approaches applied on 2,249 bug reports from two open source systems. We found empirical evidence that validates our conjecture, which led us to explore the idea of identifying and applying the best-performing approach for each bug report to obtain more accurate developer recommendations. We conducted an additional study to assess the feasibility of this idea using machine learning. While we found a wide margin of accuracy improvement for this approach, it is far from achieving the maximum possible improvement and performs comparably to baseline approaches. We discuss potential reasons for these results and conjecture that the assignment approaches may not capture important information about the bug assignment process that developers perform in practice. The results warrant future research in understanding how developers assign bug reports and improving automated bug report assignmen

Characterizing Issue Management in Runtime Systems

Modern programming languages like Java require runtime systems to support the implementation and deployment of software applications in diverse computing platforms and operating systems. These runtime systems are normally developed in GitHub-hosted repositories based on close collaboration between large software companies (e.g., IBM, Microsoft) and OSS developers. However, despite their popularity and broad usage; to the best of our knowledge, these repositories have never been studied. We report an empirical study of around 118K issues from 34 runtime system repos in GitHub. We found that issues regarding enhancement, test failure and bug are mostly posted on runtime system repositories and solution related discussion are mostly present on issue discussion. 82.69% issues in the runtime system repositories have been resolved and 0.69% issues are ignored; median of issue close rate, ignore rate and addressing time in these repositories are 76.1%, 2.2% and 58 days respectively. 82.65% issues are tagged with labels while only 28.30% issues have designated assignees and 90.65% issues contain at least one comment; also presence of these features in an issue report can affect issue closure. Based on the findings, we offer six recommenda

Should I Bug You? Identifying Domain Experts in Software Projects Using Code Complexity Metrics

In any sufficiently complex software system there are experts, having a deeper understanding of parts of the system than others. However, it is not always clear who these experts are and which particular parts of the system they can provide help with. We propose a framework to elicit the expertise of developers and recommend experts by analyzing complexity measures over time. Furthermore, teams can detect those parts of the software for which currently no, or only few experts exist and take preventive actions to keep the collective code knowledge and ownership high. We employed the developed approach at a medium-sized company. The results were evaluated with a survey, comparing the perceived and the computed expertise of developers. We show that aggregated code metrics can be used to identify experts for different software components. The identified experts were rated as acceptable candidates by developers in over 90% of all cases