Classification of software systems attributes based on quality factors using linguistic knowledge and machine learning : a review

Abstract EN

Both the functionality and the non-functionality for what the software system does and does not do within software systems requirements are documented in a Software Requirements Specification (SRS).

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 mentioned in previous literature in terms of automatic requirements extraction, then classification is performed based on methodically reviewing many previous works on software requirements classification to assist software engineers in selecting the best requirement classification technique.

The study aims to obtain answers for several questions : "What were machine learning algorithms used for the classification process of the requirements?", "How do these algorithms work and how are they evaluated?", "What methods were used for extracting features from a text?", "What evaluation criteria were used in comparing results?", and "Which machine learning techniques and methods provided the highest accuracy?".