Parallel Implementation of a Bug Report Assignment Recommender Using Deep Learning

Florea, Adrian-Cătălin; Anvik, John; Andonie, Răzvan

doi:10.1007/978-3-319-68612-7_8

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Due to their structure, sequence models were used here to model bug tossing -bug reassignment from one developer to another, dependency of developer experience, and probable number of developer participants in bug fixing, and developer reputation. Additionally, these models were employed to extract word sequences, semantic and syntactic features from bug report textual contents (Florea, Anvik and Andonie, 2017).…”

Section: Bug Report Triagementioning

confidence: 99%

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Deep Bug Reports Processing (DBRP): A Systematic Literature Review

Ahmad

Kholief

et al. 2023

Preprint

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Many software projects utilize Bug Tracking System (BTS) to manage and process bug reports. Over the years, the number of bug report submissions has increased exponentially with some projects receiving as many as about a hundred submissions daily. Bug report processing (BRP) consists of five key processes: duplicate detection, severity prediction, fix-time prediction, bug triage, and bug localization. Previously, traditional machine learning (ML) algorithms -based models were proposed to automate BRP tasks. However, in recent years deep BRP (DBRP) models were proposed to exploit the ever-increasing bug repositories for automatic extraction of semantic and contextual features of bug reports. Although, some papers reviewed related literature from many perspectives of software maintenance, no existing work comprehensively reviewed the state of DBRP. In this paper, we review the state of DBRP models in the five key BRP tasks. For this, we collect papers from four international databases published between 2015 and 2021. We evaluate the papers with regard to the deep neural networks, text representation models, and bug report features utilized. Finally, we present findings and prospects for future research works in DBRP. Our review analyzes how word embedding, CNNs, LSTMs, attention mechanism are used to represent bug report and source code, model performance and challenges such as data imbalance, feature utilization and complexity.

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Section: Bug Report Triagementioning

confidence: 99%

“…Doc2Vec is another model based on Word2Vec used to convert variable length sentences to numerical vectors. The papers in (Liu et al, 2019;Florea et al, 2017;Hamdy and Ezzat, 2020) noted the efficacy of this model for DBRP. 2.…”

Section: Text Representation For Dbrpmentioning

confidence: 99%

Deep Bug Reports Processing (DBRP): A Systematic Literature Review

Ahmad

Kholief

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…They evaluate their method using actual datasets consisting of Netbean, Eclipse, and Mozilla projects. Popular deep-learningbased methods, which machine-learning type approaches, have recently been proposed using two deep-learning classifiers, namely, convolutional and recurrent neural networks for a parallel and extendable recommending system [72], and using a convolutional neural network and word embedding for automated bug triage [73]. These studies use an actual open-source dataset and demonstrate a higher accuracy than existing machinelearning-based methods.…”

Section: Bug Report Triagementioning

confidence: 99%

Improving bug report triage performance using artificial intelligence based document generation model

Lee

Seo

2020

Hum. Cent. Comput. Inf. Sci.

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Along with the fourth industrial revolution, artificial intelligence, big data, Internet of Things, and cloud computing are emerging as cutting-edge technologies globally. In particular, artificial intelligence has unlimited potential to further improve the quality of human life and can solve several difficult engineering problems [1-12]. Moreover, this technology provides basic ideas to derive successful solutions to numerous problems encountered in the software development field.

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Guidelines for evaluating bug‐assignment research

Sajedi‐Badashian

Stroulia

2020

J Software Evolu Process

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Bug assignment is the task of ranking candidate developers in terms of their potential competence to fix a bug report. Numerous methods have been developed to address this task, relying on different methodological assumptions and demonstrating their effectiveness with a variety of empirical studies with numerous data sets and evaluation criteria. Despite the importance of the subject and the attention it has received from researchers, there is still no unanimity on how to validate and comparatively evaluate bug‐assignment methods and, often times, methods reported in the literature are not reproducible. In this paper, we first report on our systematic review of the broad bug‐assignment research field. Next, we focus on a few key empirical studies and review their choices with respect to three important experimental‐design parameters, namely, the evaluation metric(s) they report, their definition of who the real assignee is, and the community of developers they consider as candidate assignees. The substantial variability on these criteria led us to formulate a systematic experiment to explore the impact of these choices. We conducted our experiment on a comprehensive data set of bugs we collected from 13 long‐term open‐source projects, using a simple Tf‐IDf similarity metric. On the basis of our arguments and/or experiments, we provide useful guidelines for performing further bug‐assignment research. We conclude that mean average precision (MAP) is the most informative evaluation metric, the developer community should be defined as “all the project members,” and the real assignee should be defined as “any developer who worked toward fixing a bug.”

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Parallel Implementation of a Bug Report Assignment Recommender Using Deep Learning

Cited by 7 publications

References 12 publications

Deep Bug Reports Processing (DBRP): A Systematic Literature Review

Deep Bug Reports Processing (DBRP): A Systematic Literature Review

Improving bug report triage performance using artificial intelligence based document generation model

Guidelines for evaluating bug‐assignment research

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