Bug resolution catalysts: Identifying essential non-committers from bug repositories

Mani, Senthil; Nagar, Seema; Mukherjee, Debdoot; Narayanam, Ramasuri; Sinha, Vibha Singhal; Nanavati, Amit A.

doi:10.1109/msr.2013.6624027

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…Mani et al [22] analysed some participating roles in the bug fixing process. Although developers are the most important actors in this field, other participants have a significant role such as the reporters, bug tossers who reassign the bugs to the other developers, and those who have a deep technical knowledge of the software.…”

Section: Related Workmentioning

confidence: 99%

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Predicting the bug fixing time using word embedding and deep long short term memories

2020

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In bug fixing process, estimating the 'Time to Fix Bug' is one of the factors that helps the triager to allocate jobs in a better way. Due to the limitation of resources for bug fixing, the bugs with long fixing time must be identified, as soon as possible, after receiving the report. This helps the prioritisation and fixing process of the bug reports. In the process of bug fixing, a temporal sequence of activities is done. Each activity is represented by a term. Useful semantic information and longterm dependency are available between terms in the sequence, but it is usually underutilised by existing bug fixing time predictor approaches. This work presents a novel deep learning-based model (called DeepLSTMPred) that (i) converts constituent terms to a vector of real numbers by considering their semantic meaning, (ii) finds the long-term dependencies between terms by deep long short term memory (LSTM) and (iii) classifies sequences to short fixing time or long fixing time. DeepLSTMPred is evaluated on bug reports extracted from the Mozilla project. The results show that the proposed method has better performance in comparison with a state-of-the-art approach (that is the hidden Markov-based model). The experimental results show that DeepLSTMPred achieves 15-20% improvement in terms of accuracy, precision, f-score, and recall.

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Section: Related Workmentioning

confidence: 99%

“…The effect of participants in the bug fixing process and the interaction among them (e.g. developers, triage users) is also not hidden from researchers and by forming a social network of user's interactions, the effectiveness of the mentioned parameter has been investigated in [2, 5, 6, 8, 12, 14, 21–23].…”

Section: Introductionmentioning

confidence: 99%

Predicting the bug fixing time using word embedding and deep long short term memories

2020

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Predicting the Fixer of Software Bugs via a Collaborative Multiplex Network: Two Case Studies

Huang

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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GCC-Git Change Classifier for Extraction and Classification of Changes in Software Systems

Kaur

Chopra

2017

Lecture Notes in Networks and Systems

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Bug resolution catalysts: Identifying essential non-committers from bug repositories

Cited by 9 publications

References 16 publications

Predicting the bug fixing time using word embedding and deep long short term memories

Predicting the bug fixing time using word embedding and deep long short term memories

Predicting the Fixer of Software Bugs via a Collaborative Multiplex Network: Two Case Studies

GCC-Git Change Classifier for Extraction and Classification of Changes in Software Systems

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