Requirements for implemented system can be extracted and reused for a production of a new similar... more 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.
Software requirements that exist in natural language can easily be understood by various stakehol... more Software requirements that exist in natural language can easily be understood by various stakeholders. However, when it comes to extracting common requirements from the natural language requirement documents for reuse, manual extraction process can be arduous, expensive, and very error-prone on the results. In this paper, we describe a process of identifying similar requirement documents for reuse in Software Product Lines. Online product reviews were extracted and used as the input mainly due to the scarcity of publicly available requirement documents. Latent Semantic Analysis technique from Information Retrieval was used to identify similar requirement documents and filter out the unrelated ones after the text has been pre-processed. Similar documents were then clustered together by using K-means and Hierarchical Agglomerative Clustering algorithm. As a result, the output from the clustering process will be used to recommend group of related requirement documents to be used in requirement engineering phase for a SPL development.
Requirements for implemented system can be extracted and reused for a production of a new similar... more 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.
Software requirements that exist in natural language can easily be understood by various stakehol... more Software requirements that exist in natural language can easily be understood by various stakeholders. However, when it comes to extracting common requirements from the natural language requirement documents for reuse, manual extraction process can be arduous, expensive, and very error-prone on the results. In this paper, we describe a process of identifying similar requirement documents for reuse in Software Product Lines. Online product reviews were extracted and used as the input mainly due to the scarcity of publicly available requirement documents. Latent Semantic Analysis technique from Information Retrieval was used to identify similar requirement documents and filter out the unrelated ones after the text has been pre-processed. Similar documents were then clustered together by using K-means and Hierarchical Agglomerative Clustering algorithm. As a result, the output from the clustering process will be used to recommend group of related requirement documents to be used in requirement engineering phase for a SPL development.
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