Derin Öğrenme ile Otomatik Hata Triyajlama: Bir Replikasyon Çalışması

Abstract

Bug management is the process to identify and fix bugs. In the bug management process, after a bug is identified, it needs to triaged. Bug triaging is the process of prioritizing bugs and assigning an appropriate developer for a given bug. The main task in bug triaging is to predict the most appropriate developer to fix a software bug from a given bug report. This problem can be defined as a classification problem in which textual bug attributes (bug title, description etc.) are inputs and the available developer (class label) is the output. Since manual bug triaging is a time consuming process, there have een several bug triaging algorithms to automate this process. One of the latest successful algorithms to address this problem is the Deep Triage. It employs Deep Bidirectional Recurrent Neural Network with Attention (DBRNN-A) approach for this classification task. In this study, we implement an improved version of the automated bug triaging method, DeepTriage. To improve the performance of the model, three contributions are made to the original implementation: (1) Using GRU instead of LSTM to fasten the training process by using a larger batch size with the same memory usage, (2) Using a corpus combining the data from different datasets to create a more generalized model, (3) Adding extra dense layers before the multiclass classification to improve the results. After running the experiments, we achieved the state of the art results in Mozilla Firefox dataset, an accuracy of 46.6%. In the Chromium dataset, we get a higher accuracy (44.0%) than the original accuracy from the paper (42.7%). The resulting model and its source code is made publicly available for future research in this area.