DeepTriage

Mani, Senthil; Sankaran, Anush; Aralikatte, Rahul

doi:10.1145/3297001.3297023

Cited by 87 publications

(80 citation statements)

References 20 publications

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“…After achieving similar results with the paper (Mani et al, 2019), we tried some additions and changes to improve the DeepTriage method in the scope of this study. Since the accuracy results for each chronological cross validation step are shared in the paper, we use the same validation method in our experiments to be able to compare our results.…”

Section: Improvements On Deep Triagementioning

confidence: 90%

“…Xuan et al (Xuan, Jiang, Ren, Yan, & Luo, 2010) proposed a semi-supervised text classification model for bug triaging. Different from the previous studies, Deep Triage (Mani et al, 2019) extracts a novel bug report representation from bug reports by using an Attention Based Deep Bidirectional Recurrent Neural Network (DBRNN-A). These extracted features are used as input to multi-class classifier in order to predict the best developer.…”

Section: Automated Bug Triaging Studiesmentioning

confidence: 99%

“…Our dataset consists of bug reports from three open-source systems: Google Chromium, Mozilla Core and Mozilla Firefox. The details of data collecting process are mentioned in the paper (Mani et al, 2019). The authors provide the dataset available online.…”

Section: Datasetmentioning

confidence: 99%

“…In the original study (Mani et al, 2019), the architecture consists of LSTM units. It is known that the GRU (Gated Recurrent Unit) can result with a faster training process as it has less parameters than the LSTM unit.…”

Section: Using Gru Instead Of Lstmmentioning

confidence: 99%

“…One of the latest successful algorithms to address this problem is the Deep Triage. In this study, we implemented the automated bug triaging model proposed by Mani et al (Mani et al, 2019) and enhance the results. Consequently, we introduced the following three contributions to the original study and manage to achieve slightly better results:…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

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Derin Öğrenme ile Otomatik Hata Triyajlama: Bir Replikasyon Çalışması

Tüzün¹,

Doğan²,

Çetin³

2021

European Journal of Science and Technology

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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 been 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 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.

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Section: Improvements On Deep Triagementioning

confidence: 90%

Section: Automated Bug Triaging Studiesmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Using Gru Instead Of Lstmmentioning

confidence: 99%

Section: Conclusion and Recommendationsmentioning

confidence: 99%

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Derin Öğrenme ile Otomatik Hata Triyajlama: Bir Replikasyon Çalışması

Tüzün¹,

Doğan²,

Çetin³

2021

European Journal of Science and Technology

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PCG: A joint framework of graph collaborative filtering for bug triaging

Dai,

Li,

Xie

et al. 2024

J Software Evolu Process

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Bug triaging is a vital process in software maintenance, involving assigning bug reports to developers in the issue tracking system. Current studies predominantly treat automatic bug triaging as a classification task, categorizing bug reports using developers as labels. However, this approach deviates from the essence of triaging, which is establishing bug–developer correlations. These correlations should be explicitly leveraged, offering a more comprehensive and promising paradigm. Our bug triaging model utilizes graph collaborative filtering (GCF), a method known for handling correlations. However, GCF encounters two challenges in bug triaging: data sparsity in bug fixing records and semantic deficiency in exploiting input data. To address them, we propose PCG, an innovative framework that integrates prototype augmentation and contrastive learning with GCF. With bug triaging modeled as predicting links on the bipartite graph of bug–developer correlations, we introduce prototype clustering‐based augmentation to mitigate data sparsity and devise a semantic contrastive learning task to overcome semantic deficiency. Extensive experiments against competitive baselines validate the superiority of PCG. This work may open new avenues for investigating correlations in bug triaging and related scenarios.

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An Improved Classifier Based on Entropy and Deep Learning for Bug Priority Prediction

Kumari

Singh

2019

Advances in Intelligent Systems and Computing

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DeepTriage

Cited by 87 publications

References 20 publications

Derin Öğrenme ile Otomatik Hata Triyajlama: Bir Replikasyon Çalışması

Derin Öğrenme ile Otomatik Hata Triyajlama: Bir Replikasyon Çalışması

PCG: A joint framework of graph collaborative filtering for bug triaging

An Improved Classifier Based on Entropy and Deep Learning for Bug Priority Prediction

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