Abstract
Software maintenance is an important phase of a development life cycle that needs to be essentially performed in order to avoid the software failure. To systematically handle the bugs (defects), the software development organization develops a bug report that demonstrates the vulnerabilities from the software under test. However, manually handling the bug reports is a laborious, tedious, and time-consuming task. Moreover, the bug repository receives large numbers of bug reports on daily basis, which demands to timely fix the found and received bugs. Motivated by this, current work proposes an automated bug prioritization and assignment technique, called LCBPA ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> ong short-term memory, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontent-based filtering for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ug <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> rioritization and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ssignment). To perform the bug prioritization, we employed Long Short-Term Memory (LSTM) to predict the priority of the bug report. In contrast, for bug assignment, we used content-based filtering, where the prioritized bug reports are automatically assigned to the developers based on their previous knowledge. The performance of the proposed bug prioritization model is determined by comparing with the state-of-the-art bug prioritization techniques, and measured using the evaluation metrics including Precision, Recall and F1-score. Similarly, the effectiveness of the bug assignment model is evaluated by defining various case scenarios. The results show that the proposed LCBPA technique outperforms the current state-of-the-art bug prioritization techniques (with a 22% increase in F1-score), and also efficiently handles the bug assignment problem compared to the existing bug assignment techniques.
Highlights
S OFTWARE repositories play a vital role in the software development, communication, and collaboration, both in open and closed software
We propose an automated bug prioritization and assignment technique called as LCBPA
We focused on proposing a bug prioritization and assignment technique by applying an Long Short-Term Memory (LSTM) model and content-based filtering
Summary
S OFTWARE repositories play a vital role in the software development, communication, and collaboration, both in open and closed software. If the bug is new, the bug traiger checks its severity and priority in order to assign it to the most suitable developer. As reported in the literature [3], [6], [7], the exponential growth in bugs demands an automated system that classifies the bugs report keeping in view of their priority, and assigns the reported bugs to the most suitable developer. To the best of our knowledge, there is no published work that focused on effectively handling both bug assignment and bug prioritization using machine learning and deep learning techniques. Proposes an automated technique (LCBPA) for bug prioritization and assignment using LSTM and contentbased filtering.
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