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

• Alberto Oliveira ^[1] ; Ricardo Freitas ^[1] ; Jorge, Alípio ^[1] ; Vítor Amorim ^[3] ; Nuno Moniz ^[1] ; Paiva, Ana C. R. ^[1] ; Azevedo, Paulo J. ^[2]
  1. [1] Universidade Do Porto

     Universidade Do Porto

     Santo Ildefonso, Portugal

     
  2. [2] Universidade do Minho

     Universidade do Minho

     Braga (São José de São Lázaro), Portugal

     
  3. [3] RandTech Computing, R&D (Porto, Portugal)

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• Localización: Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings / Cesar Analide (ed. lit.), Paulo Novais (ed. lit.) , David Camacho Fernández (ed. lit.) , Hujun Yin (ed. lit.), Vol. 2, 2020 (Part II), ISBN 978-3-030-62365-4, págs. 516-523
• Idioma: inglés
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  • 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.