Resultados de búsqueda para Software Testing

Las pruebas de mutación permiten evaluar y mejorar la calidad de los conjuntos de casos de prueba, pero resultan costosas debido a la existencia de muchos mutantes semánticamente equivalentes al código original, lo que requiere la revisión manual de los mutantes. Este trabajo estudia la posibilidad de que estos mutantes equivalentes estén relacionados tomando como base ciertas características comunes, como son los operadores de mutación que los generan o los fragmentos de códigos en los que estos mutantes aparecen. El experimento que se muestra en este artículo ofrece evidencias de la existencia de tales conexiones, lo cual permite abordar el problema de la detección de mutantes equivalentes desde una perspectiva global en lugar de una puramente individual como hasta el momento. En base a ello, se propone un algoritmo evolutivo que utiliza esta relación entre mutantes equivalentes para resolver de forma más eficiente el problema de selección de mutantes. El trabajo fue publicado en Information and Software Technology, revista indexada en Clarivate Journal of Citation Reports con un índice de impacto de 2,726, y que se encuentra en el cuartil Q2 en la categoría Computer Science, Software Engineering (edición del índice: 2019).

Variability is a central concept in Software Product Lines (SPLs). It has been extensively studied how the SPL paradigm can improve both the efficiency of a company and the quality of products. Nevertheless, this brings several challenges when testing an SPL, which are mainly caused by the potentially huge amount of products that can be derived from an SPL. There exist different studies proposing methods for testing SPLs. Also there are secondary studies reviewing and mapping the literature of the existing proposals. Nevertheless, there is a lack of systematic guidelines for practitioners and researchers with the different steps required to perform a testing strategy of an SPL. In this paper, we present a first version of a tutorial that summarizes the existing proposals of the SPL testing area. To the best of our knowledge, there is no similar attempt in existing literature. Our goal is to discuss this tutorial with the community and enrich it to provide a more solid version of it in the future.

New processing models are being adopted in Big Data engineering to overcome the limitations of traditional technology. Among them, MapReduce stands out by allowing for the processing of large volumes of data over a distributed infrastructure that can change during runtime. The developer only designs the functionality of the program and its execution is managed by a distributed system. As a consequence, a program can behave differently at each execution because it is automatically adapted to the resources available at each moment. Therefore, when the program has a design fault, this could be revealed in some executions and masked in others. However, during testing, these faults are usually masked because the test infrastructure is stable, and they are only revealed in production because the environment is more aggressive with infrastructure failures, among other reasons. This paper proposes new testing techniques that aimed to detect these design faults by simulating different infrastructure configurations. The testing techniques generate a representative set of infrastructure configurations that as whole are more likely to reveal failures using random testing, and partition testing together with combinatorial testing. The techniques are automated by using a test execution engine called MRTest that is able to detect these faults using only the test input data, regardless of the expected output. Our empirical evaluation shows that MRTest can automatically detect these design faults within a reasonable time.

– Revista en la que fue publicado el trabajo: IEEE TRANSACTIONS ON RELIABILITY, VOL. 67, NO. 2
– Fecha de publicación: Junio 2018
– Páginas: 538-556
– DOI: 10.1109/TR.2018.2809866
– Índice JCR: 2,729
– Cuartil: Q1

Título: Incremental test data generation for database queriesAutores: María José Suárez-Cabal, Claudio de la Riva, Javier Tuya, Raquel BlancoRevista de publicación: Automated Software EngineeringNúmero, mes y año de la publicación: 24(4), Diciembre 2017Páginas: 719-755DOI: 10.1007/s10515-017-0212-7Indicios de calidad: Factor de impacto: 2.625 (JCR, 2016) Número de citas: 2 [1] R. Blanco and J. Tuya, «Modelling Test Views for Graph Database Applications», IEEE Latin America Transactions, vol. 15, no. 7, pp. 1312-1317, 2017. doi: 10.1109/TLA.2017.7959352 [2] W. Castelein, M. Aniche, M. Soltani, A. Panichella, A. Deursen, «Search-Based Test Data Generation for SQL Queries», Proceedings of the 40th International Conference on Software Engineering (ICSE 2018)

SLACT (SLA Combinatorial Testing) tool addresses the testing of Service Level Agreements (SLAs) in the context of applications developed under the Service Oriented Architectures paradigm. From the specification of the SLA, it automatically identifies a set of test requirements that are suitably combined in order to generate feasible and executable test cases. To perform the combinations, SLACT implements standard testing techniques such as the Classification Tree Method (CTM) and Combinatorial Testing.