Using Natural Language Processing and Graph Mining to Explore Inter- Related Requirements in Software Artefacts

Software development is prone to software faults due to the involvement of multiple stakeholders especially during the fuzzy phases (requirements and design). Software inspections are commonly used in industry to detect and fix problems in requirements and design artifacts thereby mitigating the fault propagation to later phases. The requirements documented in natural language (NL) are prone to contain faults because of different vocabularies among stakeholders. This research employs various NL processing with semantic analysis (SA) and mining solutions from graph theory to NL requirements to develop inter-related requirements (IRRs) that can help identify requirements that may need similar fixes. Additionally, our approach aims at aiding requirements' engineers with fault-prone regions both pre and post inspection. Pre-inspection, our approach using IRRs help removing redundant and extraneous faults within related requirements while post-inspection, it aids engineers analyse the impact of a change in one requirement on another related requirements. So, this research aims at developing a graph of inter-related requirements using natural language processing and semantic analysis approaches on a given requirements document that can be used to aid various decisions pre and post-inspections.