Predicting the Number of Reported Bugs in a Software Repository

Jahanshahi, Hadi; Çevik, Mücahit; Başar, Ayşe

doi:10.1007/978-3-030-47358-7_31

“…Specifically, in open-source projects, many free-lancer developers, whose availability in the upcoming period is not easy to determine, may attempt to fix bugs. Moreover, the timing of bug reports may not follow a specific distribution, thus not being straightforward to predict the next bug report [Jahanshahi et al, 2020].…”

Section: Related Workmentioning

confidence: 99%

AI-based Approaches to Automating the Bug Triage Process in Open-source Issue Tracking Systems

Jahanshai

2024

Preprint

0

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<p>In software engineering practice, fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manually determining the bug-fixing schedules can be time-consuming, cumbersome, and error-prone. Therefore, many researchers have investigated ways to automate the bug triage process. This dissertation seeks to further enhance the state-of-the-art on bug triage in multiple ways. We first introduce a new tool that enables us to thoroughly investigate the past bug assignment decisions in the issue tracking systems. This way, we may better understand how the ITS evolves and what factors contribute to the bug assignment decisions of the triagers. We particularly explore the bug dependency graph, textual information of the bugs, bug arrivals to the system, developers' experience and schedules, and priority and severity of the bugs. Moreover, our tool enables researchers to compare their approach with the actual bug triage practice and also traditional bug triage models. Secondly, we design an integer programming model that determines bug assignment to the developers by incorporating multiple aspects of the bug triage problem. Specifically, we enhance existing bug triage models in the literature by simultaneously considering developers' schedules, bug dependencies, bug fixing time, suitability of the developers, developers' capacity to solve multiple bugs, and the exact date of assignment. Our approach leverages natural language processing, machine learning, and integer programming to achieve a comprehensive semi-automated solution. Lastly, we extend our analysis for automated bug triage by incorporating uncertainty into the model formulation. We propose a novel method based on approximate dynamic programming (ADP) to formulate the uncertainty in the bug arrival time and developers' schedules in the system. Our numerical study shows that our ADP-based approach improves the myopic integer programming-based solution by estimating the downstream cost of the assignments. Furthermore, as the ADP learns the underlying uncertainty associated with bugs and developers, it can be considered the first online solution for bug triage in the literature.</p>

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“…Specifically, in open-source projects, many free-lancer developers, whose availability in the upcoming period is not easy to determine, may attempt to fix bugs. Moreover, the timing of bug reports may not follow a specific distribution, thus not being straightforward to predict the next bug report [Jahanshahi et al, 2020].…”

Section: Related Workmentioning

confidence: 99%

AI-based Approaches to Automating the Bug Triage Process in Open-source Issue Tracking Systems

Jahanshai

2024

Preprint

0

View full text Add to dashboard Cite

<p>In software engineering practice, fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manually determining the bug-fixing schedules can be time-consuming, cumbersome, and error-prone. Therefore, many researchers have investigated ways to automate the bug triage process. This dissertation seeks to further enhance the state-of-the-art on bug triage in multiple ways. We first introduce a new tool that enables us to thoroughly investigate the past bug assignment decisions in the issue tracking systems. This way, we may better understand how the ITS evolves and what factors contribute to the bug assignment decisions of the triagers. We particularly explore the bug dependency graph, textual information of the bugs, bug arrivals to the system, developers' experience and schedules, and priority and severity of the bugs. Moreover, our tool enables researchers to compare their approach with the actual bug triage practice and also traditional bug triage models. Secondly, we design an integer programming model that determines bug assignment to the developers by incorporating multiple aspects of the bug triage problem. Specifically, we enhance existing bug triage models in the literature by simultaneously considering developers' schedules, bug dependencies, bug fixing time, suitability of the developers, developers' capacity to solve multiple bugs, and the exact date of assignment. Our approach leverages natural language processing, machine learning, and integer programming to achieve a comprehensive semi-automated solution. Lastly, we extend our analysis for automated bug triage by incorporating uncertainty into the model formulation. We propose a novel method based on approximate dynamic programming (ADP) to formulate the uncertainty in the bug arrival time and developers' schedules in the system. Our numerical study shows that our ADP-based approach improves the myopic integer programming-based solution by estimating the downstream cost of the assignments. Furthermore, as the ADP learns the underlying uncertainty associated with bugs and developers, it can be considered the first online solution for bug triage in the literature.</p>

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“…Bugs have been a matter of research interest in software engineering for a long time, primarily due to their role as a critical indicator of software quality (Ma et al, 2020;Jahanshahi et al, 2020). The ever-evolving challenges in Software Engineering enhance this research interest.…”

Section: Introductionmentioning

confidence: 99%

Bug Prediction Models: seeking the most efficient

Marçal,

Garcia

2024

Preprint

1

0

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Choosing the most appropriate machine learning model for bug prediction tasks is critical. This paper primarily compares the predictive power of individual models versus ensemble models. We begin by experimenting with popular single-machine learning models commonly used in bug prediction, like neural networks and support vector machines. Additionally, we test with ensemble models that combine individual models' unique strengths, aiming to maximize each's benefits. Our evaluation is based on datasets containing historical development data from well-known open-source software projects. We rely on various metrics when assessing the models, encompassing accuracy, precision, recall, and F1 score. Based on our research findings, it has been observed that ensemble models tend to outperform single models, particularly when it comes to maintaining resilience across various datasets.Nevertheless, factors like the project's complexity, data availability, and computational resources all play a role in determining whether to use single or ensemble models. This paper offers a thorough analysis of the factors to consider when selecting machine learning models and approaches for bug prediction, providing valuable insights into the field. Furthermore, it offers practical advice for professionals, enabling them to make informed choices.

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Predicting the Number of Reported Bugs in a Software Repository

Cited by 7 publications

References 23 publications

AI-based Approaches to Automating the Bug Triage Process in Open-source Issue Tracking Systems

AI-based Approaches to Automating the Bug Triage Process in Open-source Issue Tracking Systems

AI-based Approaches to Automating the Bug Triage Process in Open-source Issue Tracking Systems

Bug Prediction Models: seeking the most efficient

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