Automated Conflicts Detection of Software Requirements using Natural Language Processing

Abstract

Contradictory and inconsistent sentences in a set of requirements are known as conflicting requirements. In the Requirements Engineering phase of Software Development Life Cycle (SDLC) software requirements are gathered, analyzed, negotiated back and forth manually to come to a final requirements specification document that is free from a known problem – conflicting requirements. By automating conflict detection during requirements analysis phase, time, effort, and resources can be saved in going back and forth and checking for conflicts manually. Natural Language Processing (NLP) is a way to pre-process software requirements contextually before a manual or automated model or algorithm can be applied on them. SLR (Systematic Literature Review) has been performed to distinguish 23 papers published during 2009 to 2018. The idea is to determine conflict detection models in software requirements using NLP. Furthermore, to identify tools, models, and case studies that have been vital in conflict detection since 2009. We have identified 10 tools, 23 models, and 14 case studies that have proposed conflict detection while using NLP techniques. We gathered that there is no known automated conflict detection model in software requirements using NLP techniques and contextual rules. Finally, we applied our approach to our data set and achieved complete conflict detection by comparing manual and automated testing.