Abstract
Both The functionality and the non-functionality for what the system does as well as doesn't of software systems requirements are documented in a Software Requirements Specification (SRS). Moreover in requirements engineering, system requirements classify into several categories such as functional, quality and constraint classes. Therefore, we evaluate several machine learning approaches as well as methodologies that mentioned in previous literature in term of automatic requirements extraction, then the classification based on methodically reviewing for many previous works on software requirements classification to assist software engineers in selecting the best requirement classification technique. Therefore, the study aim to get answer for several questions that related to: What machine learning algorithms were used for the classification process of the requirements, How these algorithms work and how they're evaluated, What methods were used for extracting features from a text, What evaluation criteria were used in comparing results and What machine learning techniques and methods were provided the highest accuracy.
