A Team Allocation Technique Ensuring Bug Assignment to Existing and New Developers Using Their Recency and Expertise

نویسندگان

  • Afrina Khatun
  • Kazi Sakib
چکیده

Existing techniques allocate a bug fixing team using only previous fixed bug reports. Therefore, these techniques may lead to inactive team member allocation as well as fail to include new developers in the suggested list. A Team Allocation approach for ensuring bug assignment to both Existing and New developers (TAEN) is proposed, which uses expertise and recent activities of developers. TAEN first applies Latent Dirichlet Allocation on previous bug reports to determine the possible bug types. For new developers, TAEN identifies their preferred bug type, and adds them to the list of other developers, grouped under the identified bug types. Upon the arrival of a new bug report, TAEN determines its type and extracts the corresponding group of developers. A heterogeneous network is constructed using previous reports to find the collaborations among the extracted developers. Next, for each developer, a TAEN score is computed combining the expertise and recency of their collaborations. Finally, based on the incoming report’s severity, a team of N members is allocated using the assigned TAEN score and current workloads. A case study conducted on Eclipse Java Development Tools (JDT), shows that TAEN outperforms K-nearest-neighbor Search And heterogeneous Proximity based approach (KSAP) by improving the team allocation recall from 52.88 up to 68.51, and showing the first correct developer on average at position 1.98 in the suggested list. Besides, a lower standard deviation of workloads, 30.05 rather than 46.33 indicates balanced workload distribution by TAEN. Keywords—Bug Assignment; Team Allocation; Bug Report; Latent Dirichlet Allocation (LDA).

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

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

With the increasing number of bug reports coming into the open bug repository, it is impossible to triage bug reports manually by software managers. This paper proposes a novel approach called En-LDA (Entropy optimized Latent Dirichlet Allocation (LDA)) for automatic bug report assignment. Specifically, we propose entropy to optimize the number of topics of the LDA model and further use the ent...

متن کامل

Reviewer recommendation for pull-requests in GitHub: What can we learn from code review and bug assignment?

Context: The pull-based model, widely used in distributed software development, offers an extremely low barrier to entry for potential contributors (anyone can submit of contributions to any project, through pull-requests). Meanwhile, the project’s core team must act as guardians of code quality, ensuring that pull-requests are carefully inspected before being merged into the main development l...

متن کامل

Optimized Assignment of Developers for Fixing Bugs

Decisions on “Who should fix this bug” have substantial impact on the duration of the process and its results. Expertise and related productivity level of developers might vary up to one order of magnitude. This is even more the case if we acknowledge that fixing a bug typically requires expertise in a number of components. In this paper, optimized strategies for the assignment of the “right” d...

متن کامل

BAHA: A Novel Approach to Automatic Bug Report Assignment with Topic Modeling and Heterogeneous Network Analysis∗

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...

متن کامل

Assisting bug Triage in Large Open Source Projects Using Approximate String Matching

In this paper, we propose a novel approach for assisting human bug triagers in large open source software projects by semi-automating the bug assignment process. Our approach employs a simple and efficient n-gram-based algorithm for approximate string matching on the character level. We propose and implement a recommender prototype which collects the natural language textual information availab...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017