Developing a Tailored Framework for Systematic Literature Reviews in Machine Learning (ML-SLR): Addressing Challenges and Advancing Research

Abstract

The interdisciplinary nature, rapid evolution, and heterogeneity of machine learning (ML) research present significant challenges for conducting Systematic Literature Reviews (SLRs) effectively. This paper proposes a comprehensive framework tailored to these challenges for performing SLRs in ML research. The framework supports researchers through three essential stages. The planning stage focuses on defining precise research questions, establishing inclusion and avoidance criteria, and formulating a rigorous review protocol. It is followed by the conducting stage, which involves implementing a systematic search, selecting studies, extracting data, performing quality assessment, and synthesizing results. The final reporting stage emphasizes presenting findings comprehensively and sharing SLR artifacts to promote transparency, replicability, and reuse. The framework incorporates methodologies adapted for ML, including selecting relevant digital libraries, applying discipline-specific quality criteria, and adopting synthesis techniques suitable for ML contexts. The framework was validated in a real-world ML scenario to confirm practicality and effectiveness. The study contributes a robust tool that enhances the reliability, reproducibility, and insightfulness of Machine Learning Systematic Literature Reviews (ML-SLRs), thereby advancing the consolidation of knowledge and progress in ML research.

Ähnliche Arbeiten