A Systematic Literature Review on the Use of Machine Learning in Software Engineering

TL;DR

This paper systematically reviews how machine learning techniques are applied across various software engineering domains, highlighting current trends, key areas, and specific ML methods used in the field.

Contribution

It provides a comprehensive categorization and assessment of primary studies on ML applications in software engineering, identifying key areas and techniques.

Findings

ML is widely applied in software quality assurance, maintenance, comprehension, and documentation.

Supervised learning, unsupervised learning, and deep learning are the most common ML techniques used.

The review highlights gaps and future directions for ML in software engineering.

Abstract

Software engineering (SE) is a dynamic field that involves multiple phases all of which are necessary to develop sustainable software systems. Machine learning (ML), a branch of artificial intelligence (AI), has drawn a lot of attention in recent years thanks to its ability to analyze massive volumes of data and extract useful patterns from data. Several studies have focused on examining, categorising, and assessing the application of ML in SE processes. We conducted a literature review on primary studies to address this gap. The study was carried out following the objective and the research questions to explore the current state of the art in applying machine learning techniques in software engineering processes. The review identifies the key areas within software engineering where ML has been applied, including software quality assurance, software maintenance, software comprehension, and software documentation. It also highlights the specific ML techniques that have been leveraged in these domains, such as supervised learning, unsupervised learning, and deep learning. Keywords: machine learning, deep learning, software engineering, natural language processing, source code