Aprendizado de Máquina aplicado ao Teste de Software
Resumo
Os testes de software representam uma das principais fases para a garantia da qualidade de um sistema por tornar possível não apenas a correção, mas a identificação e prevenção de defeitos. Considerando as constantes evoluções de tecnologias, observa-se a necessidade de desenvolver novas formas de manter os testes eficazes e adaptáveis. Nesse contexto, surge o Machine Learning aplicado a testes de software, que demonstram novas possibilidades de validação de qualidade. Dessa forma, por meio de uma Revisão Rápida, buscou-se analisar trabalhos relevantes para identificar os principais métodos e ferramentas, além de vantagens e desvantagens na aplicação do Machine Learning em testes de software.
Referências
Abo-Eleneen, A. Palliyali, A. and Catal, C. (2023). The role of reinforcement learning in software testing. Information and Software Technology, v. 164.
Ajorloo, S. Jamarani, A. Kashfi, M. Kashani, Haghi M. (2024). A systematic review of machine learning methods in software testing. Applied Soft Computing, p. 111805.
Cartaxo, B. Pinto, G. e Soares, S. (2020). Rapid Reviews in Software Engineering. Chapter in Book: Contemporary Empirical Methods in Software Engineering. Springer, p.357–384.
Chan, P. Y. P. Keung, J. (2024). Validating unsupervised machine learning techniques for software defect prediction with generic metamorphic testing. IEEE Access, v. 12, p. 165155-165172.
Hourani, H. Hammad, A. Lafi, M. (2019). The impact of artificial intelligence on software testing. IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), p. 565-570.
Hoffmann, J. Frister, D. (2024). Generating software tests for mobile applications using fine-tuned large language models. IEEE/ACM International Conference on Automation of Software Test (AST). p. 76-77.
Mendoza, I. Silva F, Fernando. M, Gustavo. P, Aline. N, Vânia O. Comparative Analysis of Large Language Model Tools for Automated Test Data Generation from BDD. Simpósio Brasileiro De Engenharia De Software (SBES). Porto Alegre: Sociedade Brasileira de Computação, p. 280-290
Mehmood, A. Ilyas, M. Ahmad, M. Shi, Z. (2024). Test suite optimization using machine learning techniques: a comprehensive study. IEEE Access, v. 12, p. 168645-168671.
Mehmood, I. et al. (2023). A novel approach to improve software defect prediction accuracy using machine learning. IEEE Access, v. 11, p. 63579-63597.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, v. 33, s/n, p. 1-26.
Khangura, S. Konnyu, K. Cushman, R. Grimshaw, J. e Moher, D. (2012). Evidence Summaries: The Evolution of a Rapid Review Approach. Systematic Reviews 1, n. 10.
Santos, J. G. Maciel, R. S. P. (2024). AutomTest 3.0: An automated test-case generation tool from User Story processing powered with LLMs. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SOFTWARE (SBES), 38. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, p. 769-774.
Sofian, H.; Yunus, N. A. M.; Ahmad, R. Systematic mapping: artificial intelligence techniques in software engineering. IEEE Access, v. 10, p. 51021-51040.
Shafiq, S.; Mashkoor, A.; Mayr-dorn, C.; Egyed, A. (2021). A literature review of using machine learning in software development life cycle stages. IEEE Access, v. 9, p. 140896-140920
Khatibsyarbini, M.; et al. (2021). Trend application of machine learning in test case prioritization: a review on techniques. IEEE Access, v. 9, p. 166262-166282.
Ajorloo, S. Jamarani, A. Kashfi, M. Kashani, Haghi M. (2024). A systematic review of machine learning methods in software testing. Applied Soft Computing, p. 111805.
Cartaxo, B. Pinto, G. e Soares, S. (2020). Rapid Reviews in Software Engineering. Chapter in Book: Contemporary Empirical Methods in Software Engineering. Springer, p.357–384.
Chan, P. Y. P. Keung, J. (2024). Validating unsupervised machine learning techniques for software defect prediction with generic metamorphic testing. IEEE Access, v. 12, p. 165155-165172.
Hourani, H. Hammad, A. Lafi, M. (2019). The impact of artificial intelligence on software testing. IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), p. 565-570.
Hoffmann, J. Frister, D. (2024). Generating software tests for mobile applications using fine-tuned large language models. IEEE/ACM International Conference on Automation of Software Test (AST). p. 76-77.
Mendoza, I. Silva F, Fernando. M, Gustavo. P, Aline. N, Vânia O. Comparative Analysis of Large Language Model Tools for Automated Test Data Generation from BDD. Simpósio Brasileiro De Engenharia De Software (SBES). Porto Alegre: Sociedade Brasileira de Computação, p. 280-290
Mehmood, A. Ilyas, M. Ahmad, M. Shi, Z. (2024). Test suite optimization using machine learning techniques: a comprehensive study. IEEE Access, v. 12, p. 168645-168671.
Mehmood, I. et al. (2023). A novel approach to improve software defect prediction accuracy using machine learning. IEEE Access, v. 11, p. 63579-63597.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, v. 33, s/n, p. 1-26.
Khangura, S. Konnyu, K. Cushman, R. Grimshaw, J. e Moher, D. (2012). Evidence Summaries: The Evolution of a Rapid Review Approach. Systematic Reviews 1, n. 10.
Santos, J. G. Maciel, R. S. P. (2024). AutomTest 3.0: An automated test-case generation tool from User Story processing powered with LLMs. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SOFTWARE (SBES), 38. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, p. 769-774.
Sofian, H.; Yunus, N. A. M.; Ahmad, R. Systematic mapping: artificial intelligence techniques in software engineering. IEEE Access, v. 10, p. 51021-51040.
Shafiq, S.; Mashkoor, A.; Mayr-dorn, C.; Egyed, A. (2021). A literature review of using machine learning in software development life cycle stages. IEEE Access, v. 9, p. 140896-140920
Khatibsyarbini, M.; et al. (2021). Trend application of machine learning in test case prioritization: a review on techniques. IEEE Access, v. 9, p. 166262-166282.
Publicado
01/07/2025
Como Citar
SANTOS, Vinicius Melchior L.; SANTOS, Lidy Emanuelle M.; PASSOS, Odette M..
Aprendizado de Máquina aplicado ao Teste de Software. In: CONFERÊNCIA DE TECNOLOGIA DO ICET (CONNECTECH), 2. , 2025, Itacoatiara/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 158-168.
DOI: https://doi.org/10.5753/connect.2025.12292.
