Software testing maturity models and future using Machine Learning and artificial intelligence

O processo de testes de software é cada vez mais considerada como uma das etapas mais importantes para garantir o seu bom funcionamento e robustez. Desta forma é importante conseguirmos medir de forma qualitativa a maturidade das equipas de desenvolvimento no que diz respeito ao processo utilizado para desenvolver estes mesmos testes de software. Assim, o objetivo deste estágio é encontrar um modelo de maturidade que possa ser aplicado de uma forma prática e que sirva como um método de auto avaliação das diversas equipas de software que compõem a divisão de termotecnologia da Bosch em Portugal O futuro foi igualmente merecedor da nossa atenção no decorrer do estágio, onde procuramos ferramentas que utilizem as tecnologias mais avançadas, nomeadamente machine learning e artificial intelligence, para a definição de casos de testes de software que devem ser tidos em conta no momento da sua implementação. Por fim este relatório apresenta ainda algumas tarefas que não estavam inicialmente previstas com o objetivo de tornar esta experiência curricular ainda mais completa e aumentar o conhecimento adquirido nesta área.The software testing process is increasingly considered one of the most important steps to ensure its proper functioning and robustness. Thus, it is important to be able to qualitatively measure the maturity of development teams with respect to the process used to develop these same software tests. Thus, the goal of this internship is to find a maturity model that can be applied in a practical way and that serves as a method of self-assessment of the various software teams that make up the division of thermotechnology Bosch in Portugal. The future was also worthy of our attention during the internship, where we seek tools that use the most advanced technologies, including machine learning and artificial intelligence, for the definition of software test cases that must be taken into account when implementing them. Finally, this report also presents some tasks that were not initially foreseen with the objective of making this curricular experience even more complete and increasing the knowledge acquired in this area.Mestrado em Matemática e Aplicaçõe

A self-assessment instrument for assessing test automation maturity

Abstract Test automation is important in the software industry but self-assessment instruments for assessing its maturity are not sufficient. The two objectives of this study are to synthesize what an organization should focus to assess its test automation; develop a self-assessment instrument (a survey) for assessing test automation maturity and scientifically evaluate it. We carried out the study in four stages. First, a literature review of 25 sources was conducted. Second, the initial instrument was developed. Third, seven experts from five companies evaluated the initial instrument. Content Validity Index and Cognitive Interview methods were used. Fourth, we revised the developed instrument. Our contributions are as follows: (a) we collected practices mapped into 15 key areas that indicate where an organization should focus to assess its test automation; (b) we developed and evaluated a self-assessment instrument for assessing test automation maturity; (c) we discuss important topics such as response bias that threatens self-assessment instruments. Our results help companies and researchers to understand and improve test automation practices and processes