Using evolutive algorithms to optimise collaborative testing
Software testing plays a critical role in ensuring the reliability and quality of sorgware systems, specially in large-scale applications where failures are more likely and can have significant consequences. Despite its importance, software testing is often constrained by limited resources, such as...
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/133853 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/133853 |
| Access Level: | acceso abierto |
| Palabra clave: | 004.4(043.5) 004.421(043.5) Software Testing Genetic Algorithms Multi-Objective Optimisation Group Decision-Making Particle Swarm Optimisation Testing de Software Algoritmos Genéticos Optimización Multiobjetivo Toma de Decisiones Grupal Optimización de Enjambre de Partículas Software Informática (Informática) 1203.17 Informática 1206.01 Construcción de Algoritmos |
| Sumario: | Software testing plays a critical role in ensuring the reliability and quality of sorgware systems, specially in large-scale applications where failures are more likely and can have significant consequences. Despite its importance, software testing is often constrained by limited resources, such as time, budget, computational power, or human effort. These constraints make it impractical to execute the entire test suite, particularly for complex systems. Furthermore, the collaborative nature of testing teams introduces an additional layer of complexity, as testers have to prioritise different criteria such as code criticality, time execution, coverge, or fault detection, based on their expertise and objectives. These challenges require smart approaces to optimise test selection and bring effective collaboration among testers while meeting system requirements... |
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