Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/29453
Autoria: | Reis, J. P. dos. Brito e Abreu, F. Carneiro, G. de F. Almeida, D. |
Editor: | Fernandes, J. M., Travassos, G. H., Lenarduzzi, V., and Li, X. |
Data: | 2023 |
Título próprio: | Scientific workflow management for software quality assessment replication: An open source architecture |
Volume: | 1871 |
Título e volume do livro: | Quality of Information and Communications Technology. Communications in Computer and Information Science |
Paginação: | 1 - 14 |
Título do evento: | 16th International Conference on the Quality of Information and Communications Technology, QUATIC 2023 |
Referência bibliográfica: | Reis, J. P. dos., Brito e Abreu, F., Carneiro, G. de F., & Almeida, D. (2023). Scientific workflow management for software quality assessment replication: An open source architecture. In J. M. Fernandes, G. H. Travassos, V. Lenarduzzi, & X. Li (Eds.), Quality of Information and Communications Technology. Communications in Computer and Information Science (vol.1871, pp. 1-14). Springer. https://doi.org/10.1007/978-3-031-43703-8_1 |
ISSN: | 1865-0929 |
ISBN: | 978-3-031-43703-8 |
DOI (Digital Object Identifier): | 10.1007/978-3-031-43703-8_1 |
Palavras-chave: | Scientific workflow Software quality Quality assessment Replication Code smells Open source |
Resumo: | Replication of research experiments is important for establishing the validity and generalizability of findings, building a cumulative body of knowledge, and addressing issues of publication bias. The quest for replication led to the concept of scientific workflow, a structured and systematic process for carrying out research that defines a series of steps, methods, and tools needed to collect and analyze data, and generate results. In this study, we propose a cloud-based framework built upon open source software, which facilitates the construction and execution of workflows for the replication/reproduction of software quality studies. To demonstrate its feasibility, we describe the replication of a software quality experiment on automatically detecting code smells with machine learning techniques. The proposed framework can mitigate two types of validity threats in software quality experiments: (i) internal validity threats due to instrumentation, since the same measurement instruments can be used in replications, thus not affecting the validity of the results, and (ii) external validity threats due to reduced generalizability, since different researchers can more easily replicate experiments with different settings, populations, and contexts while reusing the same scientific workflow. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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conferenceobject_97795.pdf | 391,1 kB | Adobe PDF | Ver/Abrir |
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