Sequence Mining for Automatic Generation of Software Tests from GUI Event Traces

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Alberto Oliveira (Autor) Outra; Ricardo Freitas (Autor) Outra; Alípio Jorge (Autor) Outra; Vítor Amorim (Autor) Outra; Nuno Moniz (Autor) Outra; Ana C. R. Paiva (Autor) FEUP; Paulo J. Azevedo (Autor) Outra

Ata de Conferência Internacional

Título: Intelligent Data Engineering and Automated Learning - IDEAL 2020 - 21st International Conference - Proceedings, Part II

Páginas: 516-523

21st International Conference IDEAL 2020. International Conference on Intelligent Data Engineering and Automated Learning

Guimarães, November 4–6, 2020

Indexação

ISI Web of Science

Scopus - 2 Citações

INSPEC

Outras Informações

ID Authenticus: P-00S-ZXN

DOI: 10.1007/978-3-030-62365-4_49

Abstract (EN): 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. © 2020, Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8