AI-Powered Software Development: A Systematic Review of Recommender Systems for Programmers

Abstract

Software engineering is a field that demands extensive knowledge and involves numerous challenges in managing information. The information landscapes in software engineering encompass source code and its revision history, a set of explicit instructions for writing, commenting on and running the codes, a set of procedures and routines, and the development environment. For software engineers who develop code, writing code documentation is also extremely important. Due to the technical complexity, vast scale, and dynamic nature of software engineering, there is a need for a specialized category of tools to assist developers, known as recommendation systems in software engineering (RSSE). RSSEs are specialized software applications designed to assist developers by providing valuable resources, code snippets, solutions to problems, and other useful information and suggestions tailored to their specific tasks. Through the analysis of data and user interactions, RSSEs aim to enhance productivity and decision-making for developers. To this end, this work presents an analysis of the literature on recommender systems for programmers, highlighting the distinct attributes of RSSEs. Moreover, it summarizes all related challenges regarding developing, assessing, and utilizing RSSEs, and offers a broad perspective on the present state of research and advancements in recommendation systems for the highly technical field of software engineering.