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El autor Ana B. Sánchez ha publicado 5 artículo(s):

1 - Towards Multi-Objective Test Case generation for Variability-Intensive Systems

Testing variability-intensive systems is a challenge due to the potentially huge number of derivable configurations. To alleviate this problem, many test case selection and prioritization techniques have been proposed with the aim of reducing the number of configurations to be tested and increasing their effectiveness. However, we found that these approaches do not exploit all available information since they are mainly driven by functional information such as the feature coverage. Furthermore, most of these works are focused on a single-objective perspective (e.g. features coverage), which could not reflect the real scenarios where several goals need to be met (e.g. features coverage and code changes coverage). In this context, we identify an important challenge, to take advantage of all available system information to guide the generation of test cases. As a first step towards a solution, we propose to study all this information with special emphasis on non-functional properties and address the test case generation as a multi-objective problem. Also, we describe some open issues to be explored that we hope have an important impact on future evaluations.

Autores: Ana B. Sánchez / Sergio Segura / Antonio Ruiz-Cortés / 
Palabras Clave: extra-functional attributes - Multi-objective test generation

2 - Automated testing on the analysis of variability-intensive artifacts: An exploratory study with SAT Solvers

The automated detection of faults on variability analysis tools is a challenging task often infeasible due to the combinatorial complexity of the analyses. In previous works, we successfully automated the generation of test data for feature model analysis tools using metamorphic testing. The positive results obtained have encouraged us to explore the applicability of this technique for the efficient detection of faults in other variability-intensive domains. In this paper, we present an automated test data generator for SAT solvers that enables the generation of random propositional formulas (inputs) and their solutions (expected output). In order to show the feasibility of our approach, we introduced 100 artificial faults (i.e. mutants) in an open source SAT solver and compared the ability of our generator and three related benchmarks to detect them. Our results are promising and encourage us to generalize the technique, which could be potentially applicable to any tool dealing with variability such as Eclipse repositories or Maven dependencies analyzers.

Autores: Ana B. Sánchez / Sergio Segura / 
Palabras Clave:

3 - A Survey on Metamorphic Testing

S. Segura, G. Fraser, A. B. Sanchez and A. Ruiz-Cortés, A Survey on Metamorphic Testing, in IEEE Transactions on Software Engineering, vol. 42, no. 9, pp. 805-824, Sept. 1 2016. https://doi.org/10.1109/TSE.2016.2532875 Indicadores de calidad: – Revista de referencia en el área de Ingeniería del Software (CS-SE: 20/106). – Ha recibido 9 citas desde su publicación en febrero de 2016 (más otras 5-7 citas por aparecer en las actas del segundo workshop internacional de pruebas metamórficas [1]). – Hemos sido invitados a presentar el trabajo en ICSE17 como parte de la iniciativa journal-first (ver programa de la conferencia [2]). – Colaboración internacional con el profesor Gordon Fraser. [1] https://www.cs.montana.edu/met17/ [2] http://icse2017.gatech.edu/?q=technical-research-accepted

Autores: Sergio Segura / Gordon Fraser / Ana B. Sánchez / Antonio Ruiz-Cortés / 
Palabras Clave: metamorphic testing - oracle problem - survey

4 - Evaluación y mejora de pruebas de rendimiento utilizando mutación del software: Un enfoque evolutivo

Los errores de rendimiento del software pueden causar una importante degradación en la experiencia de usuario y dar lugar a problemas muy costosos de detectar y resolver. Las pruebas de rendimiento persiguen detectar y reducir el impacto de estos errores. Sin embargo, no existen mecanismos para evaluar la calidad de las pruebas de rendimiento, causando en muchos casos, que estos errores pasen desapercibidos. La prueba de mutación es una técnica para evaluar y mejorar las pruebas funcionales a través de la introducción de errores artificiales en el programa bajo prueba. En este artículo, exploramos la aplicabilidad de la prueba de mutación junto con el empleo de un algoritmo evolutivo para buscar mutantes que simulen errores de rendimiento. Esta propuesta noPrueba de mutación, errores de rendimiento, pruebas de rendimiento, algoritmos evolutivos.vedosa contribuye a mejorar la confianza en las pruebas de rendimiento al mismo tiempo que reduce el coste de la prueba de mutación.

Autores: Ana B. Sánchez / Pedro Delgado-Pérez / Inmaculada Medina-Bulo / Sergio Segura / 
Palabras Clave: algoritmos evolutivos - errores de rendimiento - prueba de mutación - Pruebas de Rendimiento

5 - Performance mutation testing: hypothesis and open questions

Performance bugs are common, costly, and elusive. Performance tests aim to detect performance bugs by running the program with specific inputs and determining whether the observed behaviour is acceptable. There not exist mechanisms, however, to assess the effectiveness of performance tests. Mutation testing is a technique to evaluate and enhance functional test suites by seeding artificial faults in the program under test. In this new idea paper, we explore the applicability of mutation testing to assess and improve performance tests. This novel approach is motivated with examples and open questions.Ana B. Sánchez, Pedro Delgado-Pérez, Sergio Segura, Inmaculada Medina-Bulo. Performance Mutation Testing: Hypothesis and open questions. Information and Software Technology Journal, 103, 159-161, November 2018. Disponible online: https://doi.org/10.1016/j.infsof.2018.06.015ÍNDICES DE CALIDADFactor de impacto: 2,694Categoría de la revista: Q1Posición en su categoría: 16Número de autores: 4

Autores: Ana B. Sánchez / Pedro Delgado-Pérez / Sergio Segura / Inmaculada Medina-Bulo / 
Palabras Clave: Mutation testing - performance bugs - performance testing