Creating Evolving Project Data Sets in Software Engineering

Lewowski, Tomasz; Madeyski, Lech

doi:10.1007/978-3-030-26574-8_1

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…GitHub and Open source software as sources for mining software development data Data mining on GitHub or Open source repositories has been used to create evolving project data sets for intelligent/empirical software engineering [108,109], for example, for Eclipse Modelling Framework metamodels formation [110,111] anomaly detection [112] or identification of migration reasons [113].…”

Section: Covid-19 Influence On Software Engineeringmentioning

confidence: 99%

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

Kokol

2024

Preprint

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Artificial intelligence (AI) has witnessed an exponential increase in its use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems. However, a comprehensive and holistic understanding of the current status is missing. To close the above gap, synthetic knowledge synthesis was used to induce a research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and five themes, namely natural language processing in software engineering, use of artificial intelligence in the management of software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n=2042), followed by the United States (n=1193), India (n=934), Germany (n=445), and Canada (n=381). A high percentage (n=47.4%) of papers were funded, showing a strong interest in this research topic. The convergence of AI and software engineering can significantly reduce needed resources, improve quality, increase user experience, and improve the well-being of software developers.

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Section: Covid-19 Influence On Software Engineeringmentioning

confidence: 99%

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

Kokol

2024

Preprint

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“…Since constant changes in development practices and technologies over time can produce different outcomes in ESE studies, a dataset of software projects constructed several years ago can rarely be representative of recently developed projects. Lewowski and Madeyski (2020) partially tackled this with their tool to create evolving datasets, but did not precise how to deal with deprecated projects.…”

Section: Related Workmentioning

confidence: 99%

Open-Source Software Projects Curating Model for Empirical Software Engineering Studies

Carruthers

2022

Anais Do XXV Congresso Ibero-Americano Em Engenharia De Software (CIbSE 2022)

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Software projects are common inputs in Empirical Software Engineering (ESE), and they are often selected without following a specific strategy, leading to biased samples. To avoid this problem, researchers choose to use publicly available datasets instead of picking the projects themselves. However, some datasets are not maintained, containing old versions of projects, or even deprecated ones. This may raise some representativeness issues due to major changes in development practices and technologies over time. The main goal of this research is to develop a procedures model to construct and maintain a software project dataset with their product quality metrics, to support the development of ESE studies.

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“…Irreproducibility-which may either be planned or accidental-is a serious problem in many fields of science like medicine [3], biomedical studies [4], and software engineering [5] and, in our opinion, should definitely be included as a principle to be followed. On a practical level, this would include detailed research protocols, data collection methods and datasheets or even automated scripts ran to gather data [6]. Only by including all relevant details we can verify research claims and avoid problems like comparing results obtained from two data sets that were sampled from different populations-a problem especially visible during COVID-19 pandemic, where infection Bull.…”

Section: 2mentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Madeyski,

Lewowski,

Kitchenham

2020

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Sharing research data from public funding is an important topic, especially now, during times of global emergencies like the COVID-19 pandemic, when we need policies that enable rapid sharing of research data. Our aim is to discuss and review the revised Draft of the OECD Recommendation Concerning Access to Research Data from Public Funding. The Recommendation is based on ethical scientific practice, but in order to be able to apply it in real settings, we suggest several enhancements to make it more actionable. In particular, constant maintenance of provided software stipulated by the Recommendation is virtually impossible even for commercial software. Other major concerns are insufficient clarity regarding how to finance data repositories in joint private-public investments, inconsistencies between data security and user-friendliness of access, little focus on the reproducibility of submitted data, risks related to the mining of large data sets, and sensitive (particularly personal) data protection. In addition, we identify several risks and threats that need to be considered when designing and developing data platforms to implement the Recommendation (e.g., not only the descriptions of the data formats but also the data collection methods should be available). Furthermore, the non-even level of readiness of some countries for the practical implementation of the proposed Recommendation poses a risk of its delayed or incomplete implementation.

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Creating Evolving Project Data Sets in Software Engineering

Cited by 6 publications

References 28 publications

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

Open-Source Software Projects Curating Model for Empirical Software Engineering Studies

Bulletin of the Polish Academy of Sciences: Technical Sciences

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