Abstract:
Understanding of human language text in written form by a computer tool is possible
because of Natural language processing (NLP). NLP utilizes the principles of artificial
intelligence (AI). The creation of NLP soft wares is a difficult task because computers only
understand in some programming language which is precise, unambiguous and highly
structured. Natural language text is obviously not precise. It can also be ambiguous. The
linguistic structure of the sentences can also vary a lot due to slang, regional dialects and
social context. Contradictory and inconsistent sentences in a set of requirements is 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 is a way to pre-process software
requirements contextually before a manual or automated model or algorithm can be applied
on them. A fully automated tool was developed in python language using NLTK toolkit
utilizing multiple NLP techniques to facilitate software requirement engineer in verifying the
software requirements for consistency issues and conflicts. This tool generates a traceability
matrix to identify which possible requirements have consistency issues
