Recommendation Technologies in Requirements Engineering Sample Clauses

Recommendation Technologies in Requirements Engineering. The importance of requirements engineering has been illustrated by several studies (e.g., [Xxxxx and Xxxxxx 2001], [Arnuphaptrairong 2011] and [Calleam Consulting 2015]), which have shown that requirements-related issues, such as poorly specified requirements and incomplete and changing requirements, are the root cause of many failed projects. To address these problems, researchers and practitioners have applied recommenders systems to the requirements engineering stage. In this section, we briefly describe several works in which recommendation technologies have been applied to requirements engineering (see Table 1). These approaches have been identified while doing a systematic mapping. More details about the systematic mapping and these approaches can be found in deliverable D3.1. Table 1: Recommendation technologies in requirements engineering: state-of-the-art summary Activity Recommendation Recommendation approach Reference Elicitation & Specification Requirements Content-based filtering (based on clustering) [Xxxxxxx et al. 2011] Knowledge-based (based on domain ontologies) [Xxxxx et al. 2010] Requirements (security approach) Knowledge-based [Xxxxxx and Xxxxxxxxxx 2004] Requirements, Stakeholders Collaborative filtering, Content-based filtering [Xxxxxx-Xxxxxxx et al. 2009, 2010] Collaborative filtering, Social network analysis [Xxx and Xxxxxxxxxxx 2012] Quality assurance Completeness Content-based filtering (based on clustering) [Xxxxxxx et al. 2011] Consistency Knowledge-based [Xxxxxxxxx et al. 2013] Redundancy Clustering [Xxxxxxx-Xxxxx et al. 2009] Negotiation & Planning Prioritization Clustering [Xxxxxxx-Xxxxx et al. 2009] Maintenance Contributors Collaborative filtering (based on Markov chains) [Xxxxx et al. 2009] Text mining (similarity) [Xxxx et al. 2007] Text mining (similarity) [Xxxxxxx and Verma 2012] Classification [Xxxxxxxx et al. 2010] Classification (naive bayes) [Xxxxxxxx and Xxxxxx 2014] Classification (support vector machines) [Xxxxx et al. 2006] Effort Text mining (similarity) [Xxxx and Xxxxx 2009] Requirements Classification [Xxxxxxxx et al. 2008] Severity level Classification [Xxxxxxxxxx et al. 2011] Classification (naive bayes) [Xx Xxxx et al. 2002] Text mining (machine learning) [Xxxxxxx and Xxxxxx 2008] As can be seen in Table 1, the approaches about recommender systems in the field of requirements engineering deal with the following activities: ● Elicitation and Specification of requirements. In this case, the main aim is to re...