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  1. 1. APPLYING MACHINE LEARNING ALGORITHMS TO DETECT LINES OF CODE CONTRIBUTING TO TECHNICAL DEBT

    Kandidat-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :Filip Isakovski; Rafael Antonino Sauleo; [2019-11-12]
    Nyckelord :Technical Debt; Machine Learning; Static Code Analysis;

    Sammanfattning : This paper shows the investigation of the viability of finding lines of code (LOC) contributing to technical debt (TD) using machine learning (ML), by trying to imitate the static code analysis tool SonarQube. This is approached by letting industry professionals choose the SonarQube rules, followed by training different classifiers with the help of CCFlex (a tool for training classifiers with lines of code), while using SonarQube as an oracle (a source of training sample data) which selects the faulty lines of code. LÄS MER