An Experimental Analysis on Automated Machine Learning for Software Defect Prediction

The widespread use of machine learning (ML) in software engineering (SE) encounters a notable challenge: the need for various domain-specific parameters in algorithms. The issue arises when attempting to reuse these parameters across different applications, resulting in sub-optimal outcomes. This hindrance significantly contributes to the limited migration of ML solutions from research labs to industrial settings. This paper underscores the pressing need for novel research to tackle the overarching problem of generic algorithm customisation. To address this, we propose leveraging Automated Machine Learning (AutoML) approaches. These techniques automatically select intelligible models and their corresponding hyper-parameters for forecasting software defect-proneness. More specifically, this paper adapts an AutoML approach to the field of software defect prediction, namely a hyper-heuristic evolutionary algorithm for automatically designing decision tree algorithms (HEAD-DT), originally proposed to address a generic optimisation problem. We benchmark against the popular general software defect-proneness prediction framework (GSDP) and some standard classifiers. Experimental results reveal that the proposed HEAD-DT implementation surpasses other algorithms across three distinct evaluation measures.