Sequence mining for automatic generation of software tests from GUI event traces

In today’s software industry, systems are constantly changing. To maintain their quality and to prevent failures at controlled costs is a challenge. One way to foster quality is through thorough and systematic testing. Therefore, the definition of adequate tests is crucial for saving time, cost and...

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Detalhes bibliográficos
Autor principal: Oliveira, Alberto (author)
Outros Autores: Freitas, Ricardo (author), Jorge, Alípio (author), Amorim, Vítor (author), Moniz, Nuno (author), Paiva, Ana C.R. (author), Azevedo, Paulo J. (author)
Formato: conferencePaper
Idioma:eng
Publicado em: 2020
Assuntos:
Data mining
Frequent pattern mining
Markov chains
Software testing
Texto completo:http://hdl.handle.net/1822/71380
País:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/71380
Descrição
Resumo:In today’s software industry, systems are constantly changing. To maintain their quality and to prevent failures at controlled costs is a challenge. One way to foster quality is through thorough and systematic testing. Therefore, the definition of adequate tests is crucial for saving time, cost and effort. This paper presents a framework that generates software test cases automatically based on user interaction data. We propose a data-driven software test generation solution that combines the use of frequent sequence mining and Markov chain modeling. We assess the quality of the generated test cases by empirically evaluating their coverage with respect to observed user interactions and code. We also measure the plausibility of the distribution of the events in the generated test sets using the Kullback-Leibler divergence.

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