A model of open source software maintenance activities

Xiong, Chengjie; Li, Y. F.; Xie, Min; Ng, Szu Hui; Goh, T. N.

doi:10.1109/ieem.2009.5373367

Cited by 8 publications

(1 citation statement)

References 17 publications

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“…However, maintenance activities have never been a straightforward task, it is considered as the most time/effort-consuming and complex task throughout the life cycle of software development [7]. The maintenance practices for traditional software systems have been extensively studied [8,9,10,11,7,12,13,14,15]. These studies focus on a broad range of areas, including examining source-code complexity and software enhancement effort [16], estimating the reliability in the maintenance phase [17], studying the distributions and evolution of technical debts [18,19,20,21], and predicting the prevalence of bugs using anti-patterns [22,23,24] among others.…”

Section: Introductionmentioning

confidence: 99%

Technical Debts and Faults in Open-source Quantum Software Systems: An Empirical Study

Openja¹,

Morovati²,

An³

et al. 2022

Preprint

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Quantum computing is a rapidly growing field attracting the interest of both researchers and software developers. Supported by its numerous open-source tools, developers can now build, test, or run their quantum algorithms. Although the maintenance practices for traditional software systems have been extensively studied, the maintenance of quantum software is still a new field of study but a critical part to ensure the quality of a whole quantum computing system. In this work, we set out to investigate the distribution and evolution of technical debts in quantum software and their relationship with fault occurrences. Understanding these problems could guide future quantum development and provide maintenance recommendations for the key areas where quantum software developers and researchers should pay more attention. In this paper, we empirically studied 118 open-source quantum projects, which were selected from GitHub. The projects are categorized into 10 categories. We found that the studied quantum software suffers from the issues of code convention violation, error-handling, and code design. We also observed a statistically significant correlation between code design, redundant code or code convention, and the occurrences of faults in quantum software. Highlights• Quantum software suffers from code convention, error handling, and code redundancy.• There is a correlation between technical debts and fault in quantum software.• Quantum developers should use existing static analysis tools to examine their code.• New tools should be introduced to support identifying quantum-specific problems.• Future works should study other aspects of maintenance and reliability of quantum.

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Section: Introductionmentioning

confidence: 99%

Technical Debts and Faults in Open-source Quantum Software Systems: An Empirical Study

Openja¹,

Morovati²,

An³

et al. 2022

Preprint

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On the value of instance selection for bug resolution prediction performance

Miloudi,

Cheikhi,

Idri

et al. 2024

J Software Evolu Process

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Software maintenance is a challenging and laborious software management activity, especially for open‐source software. The bugs reports of such software allow tracking maintenance activities and were used in several empirical studies to better predict the bug resolution effort. These reports are known for their large size and contain nonrelevant instances that need to be preprocessed to be suitable for use. To this end, instance selection (IS) has been proposed in the literature as a way to reduce the size of the datasets, while keeping the relevant instances. The objective of this study is to perform an empirical study that investigates the impact of data preprocessing through IS on the performance of bug resolution prediction classifiers. To deal with this, four IS algorithms, namely, edited nearest neighbor (ENN), repeated ENN, all‐k nearest neighbors, and model class selection, are applied on five large datasets, together with five machine learning techniques. Overall, 125 experiments were performed and compared. The findings of this study highlight the positive impact of IS in providing better estimates for bug resolution prediction classifiers, in particular using repeated ENN and ENN algorithms.

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