Applying Convolutional Neural Networks to Adjust Code Complexity Metrics
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Palavras-chave

Software testing
Software defects
Complexity metrics

Como Citar

SILVA, Nykson da; VILELA, Plinio. Applying Convolutional Neural Networks to Adjust Code Complexity Metrics. Revista dos Trabalhos de Iniciação Científica da UNICAMP, Campinas, SP, n. 27, p. 1–1, 2019. DOI: 10.20396/revpibic2720191833. Disponível em: https://econtents.bc.unicamp.br/eventos/index.php/pibic/article/view/1833. Acesso em: 29 mar. 2024.

Resumo

In the software development process, testing activities are responsible for identifying the presence of defects and allowing them to be repaired before delivery to the end user. Previous research on cost reduction strategies attributed to software testing, focused on test case prioritization techniques based on code size and complexity metrics in order to reduce the cost of software and still guarantee the ability to detect defects. However, these studies did not considered aspects related to the developer.

https://doi.org/10.20396/revpibic2720191833
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Referências

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GAROUSI, V.; YASAMAN, A.; IRVING, R. A Search-Based Approach for Cost-Effective Software Test Automation Decision Support and an Industrial Case Study. Wageningen: IEEE International Conference on Software Testing, Verification, and Validation Workshops, 2014.

KORU, A. G.; ZHANG, D.; EMAM, E. K. An Investigation into the Functional Form of the Size-Defect Relationship for Software Modules. Baltimore: IEEE Transactions on Software, 2009.

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