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A general approach to Software Product Line testing

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.

Automatic Testing of Design Faults in MapReduce Applications

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.

Early Integration Testing for Entity Reconciliation in the Context of Heterogeneous Data Sources

– 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

ARTICULO RELEVANTE:Incremental test data generation for database queries

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: a Test Case Generation Tool for Service Level Agreements

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.