Machine Learning-Enhanced Requirements Engineering: A Systematic Literature Review

In the software lifecycle, requirements are often subjective and ambiguous, challenging developers to comprehend and implement them accurately and thoroughly. Nevertheless, using techniques and knowledge can help analysts simplify and improve requirements comprehensibility, ensuring that the final product meets the client’s expectations and needs. The Requirements Engineering domain and its relationship to Machine Learning have gained momentum recently. Machine Learning algorithms have shown significant progress and superior performance when dealing with functional and non-functional requirements, natural language processing, text-mining, data-mining, and requirements extraction, validation, prioritisation, and classification. This paper presents a Systematic Literature Review identifying novel contributions and advancements from January 2012 to June 2023 related to strategies, technology and tools that use Machine Learning techniques in Requirements Engineering. This process included selecting studies from five databases (Scopus, WoS, IEEE, ACM, and Proquest), from which 74 out of 1219 were selected. Although some successful applications were found, there are still topics to explore, such as analysing requirements using different techniques, combining algorithms to improve strategies, considering other requirements specification formats, extending techniques to larger datasets and other application domains and paying attention to the efficiency of the approaches.