Feature extraction approaches from natural language requirements for reuse in software product lines: A systematic literature review

• Hybrid NLP approaches were more common for extracting textual requirements.
• There is a mixture of automated and semi-automated approaches from IR and data mining.
• Support tools were not made available to the public.
• Not all studies use software metrics in conjunction with experiments and case studies.
• Reconfirm practitioners guidelines’ absence from selected studies (Alves et al., 2010).

Requirements for implemented system can be extracted and reused for a production of a new similar system. Extraction of common and variable features from requirements leverages the benefits of the software product lines engineering (SPLE). Although various approaches have been proposed in feature extractions from natural language (NL) requirements, no related literature review has been published to date for this topic. This paper provides a systematic literature review (SLR) of the state-of-the-art approaches in feature extractions from NL requirements for reuse in SPLE. We have included 13 studies in our synthesis of evidence and the results showed that hybrid natural language processing approaches were found to be in common for overall feature extraction process. A mixture of automated and semi-automated feature clustering approaches from data mining and information retrieval were also used to group common features, with only some approaches coming with support tools. However, most of the support tools proposed in the selected studies were not made available publicly and thus making it hard for practitioners’ adoption. As for the evaluation, this SLR reveals that not all studies employed software metrics as ways to validate experiments and case studies. Finally, the quality assessment conducted confirms that practitioners’ guidelines were absent in the selected studies.