Candidato: Francisco Gomes de Oliveira Neto
Título do trabalho: Investigation of Similarity-based Test Case Selection for Speciﬁcation-based Regression Testing
Orientador(es): Patricia Duarte de Lima Machado
Banca examinadora: Patricia Duarte de Lima Machado (orientadora), Adenilso da Silva Simão (Universidade de São Paulo), Eduardo Henrique da Silva Aranha (UFRN), Tiago Lima Massoni (UFCG), Emanuela Gadelha Cartaxo (UFCG).
Resumo: During software maintenance, several modiﬁcations can be performed in a speciﬁcation model in order to satisfy new requirements. Perform regression testing on modiﬁed software is known to be a costly and laborious task. Test case selection, test case prioritization, test suite minimisation, among other methods, aim to reduce these costs by selecting or prioritizing a subset of test cases so that less time, effort and thus money are involved in performing regression testing. In this doctorate research, we explore the general problem of automatically selecting test cases in a model-based testing (MBT) process where speciﬁcation models were modiﬁed. Our technique, named Similarity Approach for Regression Testing (SART), selects subset of test cases traversing modiﬁed regions of a software system’s speciﬁcation model. That strategy relies on similarity-based test case selection where similarities between test cases from different software versions are analysed to identify modiﬁed elements in a model. In addition, we propose an evaluation approach named Search Based Model Generation for Technology Evaluation (SBMTE) that is based on stochastic model generation and search-based techniques to generate large samples of realistic models to allow experiments with model-based techniques. Based on SBMTE, researchers are able to develop model generator tools to create a space of models based on statistics from real industrial models, and eventually generate samples from that space in order to perform experiments. Here we de- veloped a generator to create instances of Annotated Labelled Transitions Systems (ALTS), to be used as input for our MBT process and then perform an experiment with SART. In this experiment, we were able to conclude that SART’s percentage of test suite size reduction is robust and able to select a subset with an average of 92% less test cases, while ensuring coverage of all model modiﬁcation and revealing defects linked to model modiﬁcations. Both SART and our experiment are executable through the LTS-BT tool, enabling researchers to use our selection strategy and reproduce our experiment.