Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization

• Main Authors: Onyeka, Emebo, Varde, Aparna S., Anu, Vaibhav, Tandon, Niket, Daramola, Olawande
• Format: Conference Proceeding
• Language: English
• Subjects: AI in Smart Cities; Commonsense Knowledge; Implicit Requirements; Ontologies; Ontology; Requirements engineering; Smart cities; Software Demo; Software tools; Task analysis; Text mining
• ISSN: 2375-0197
• DOI: 10.1109/ICTAI50040.2020.00146

This paper addresses identification of implicit requirements (IMRs) in software requirements specifications (SRS). IMRs, as opposed to explicit requirements, are not specified by users but are more subtle. It has been noticed that IMRs are crucial to the success of software development. In this paper, we demonstrate a software tool called COTIR developed by us as a system that integrates Commonsense knowledge, Ontology and Text mining for early identification of Implicit Requirements. This relieves human software engineers from the tedious task of manually identifying IMRs in huge SRS documents. Our evaluation reveals that COTIR outperforms existing IMR tools. This demo paper would be useful to Software Engineers since it deals with automation in the requirements analysis phase, thus contributing to Requirements Engineering. It would interest AI scientists as it entails multi-disciplinary work encompassing text mining, ontology and commonsense knowledge. It makes a broader impact on Smart Cities, because automated identification of IMRs would offer inputs to Smart City Tools, where requirements may often be implicit given that Smart Cities are an emerging and growing paradigm.