Improving Software Fault Prediction Using a Data-Driven Approach
Improving Software Fault Prediction Using a Data-Driven Approach André Sobral Gonçalves Thesis to obtain the Master of Science Degree in Information Systems and Computer Engineering Supervisor: Prof. Rui Filipe Lima Maranhão de Abreu Examination Committee: Chairperson: Prof. Daniel Jorge Viegas Gonçalves Supervisor: Prof. Rui Filipe Lima Maranhão de Abreu Members of the Committee: Prof. João Fernando Peixoto Ferreira September 2019 ii Acknowledgements Firstly, I want to thank my supervisor, Professor Rui Maranhão for his insight, guidance, patience and for having given me the opportunity to do research in such an interesting field. I am grateful to my family for their immense support, patience and love throughout the entire course. In particular, to my parents, João Carlos and Ana Maria Gonçalves, my brother, João Pedro Gonçalves, my parents-in-law, Carlos and Maria José Alves and my beautiful niece Olivia. I want to thank Carolina Alves, for all her love, care, kindness, determination and willingness to review countless times everything I needed. To my friends and colleagues, José Ferreira, Gustavo Enok, Fábio Almeida, Duarte Meneses, Delfim Costa for supporting me all this time. iii Abstract In software development, debugging is one of the most expensive tasks in the life cycle of a project. Repetitive, manual identification of faults in software, which are the source of errors and inefficiencies, is inherently time-consuming and error-prone. The solution to this problem is automated software diagnosis. This approach solves the issues mentioned previously as long as the accuracy of the diagnosis is credible. There are different tools and techniques which ensure this diagnosis, but they neglect valuable data.
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