Mecanismos de apoio ao teste exploratório de software: mapa de oportunidades e chatbots

• Ano de defesa: 2022
• Autor(a) principal: Borzani, Rubens Copche
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Cornelio Procopio; Brasil; Programa de Pós-Graduação em Informática; UTFPR
• Palavras-chave em Português: Mapas cognitivos (Psicologia); Software - Testes; Software - Desenvolvimento; Cognitive maps (Psychology); Computer software - Testing; Computer software - Development; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO; Ciência da Computação
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/30205

Context: The demand for software development has increased every day to solve both corporate and personal issues. With this increase, more agile ways are sought to deliver a quality software product that meets the requirements effectively. To do so, it is necessary to test the software. One of the approaches used to test software is Exploratory Testing (ET), a manual technique that does not use scripts. To conduct it properly, some rules must be followed because it is not an ad hoc test. As a manual test, the literature points out to ideas and tools to support and/or manage it. Objective: This dissertation aims to understand whether mechanisms to support exploratory testing can improve its effectiveness. For this, an Opportunity Map (OM) and a Chatbot were used as supporting tools during the test sessions. Method: Based on the data obtained in the experiments, we aimed to identify whether these tools bring better results to the Exploratory Testing. To evaluate the use of OMs, a study was conducted with 22 participants and compared the proposed approach with a traditional ET approach. To evaluate the use of the chatbot, another study was conducted with 6 participants, seeking to understand the participants' perception of the chatbot adoption. Results: As for OMS, the results indicated that the number of bugs detected was similar in both approaches, different bugs were revealed by each approach, and the OMs tended to guide the detection of specific bugs. The study with the chatbot gave evidence that the user interactions with the chatbot in the training session sought to learn and obtain knowledge of the tool. In the test sessions, most interactions were performed after bugs and issues were detected. All participants were satisfied with the use of the chatbot in the ET. Conclusion: The proposed mechanisms had positive results in the exploratory test and motivate future research on the subject.