Common Sense Knowledge, Ontology and Text Mining for Implicit Requirements
This addresses a gap in practice for software developers and organizations by providing an automated tool to handle unelicited requirements, though it appears incremental as it builds on existing methods like text mining and ontology.
The paper tackles the problem of implicit requirements (IMRs) in software systems, which are assumed needs not elicited during requirements gathering and a major cause of system failure, by proposing a novel approach that combines common sense knowledge, text mining, and ontology to automatically identify and manage IMRs, aiming to reduce risks and costs in software development.
The ability of a system to meet its requirements is a strong determinant of success. Thus effective requirements specification is crucial. Explicit Requirements are well-defined needs for a system to execute. IMplicit Requirements (IMRs) are assumed needs that a system is expected to fulfill though not elicited during requirements gathering. Studies have shown that a major factor in the failure of software systems is the presence of unhandled IMRs. Since relevance of IMRs is important for efficient system functionality, there are methods developed to aid the identification and management of IMRs. In this paper, we emphasize that Common Sense Knowledge, in the field of Knowledge Representation in AI, would be useful to automatically identify and manage IMRs. This paper is aimed at identifying the sources of IMRs and also proposing an automated support tool for managing IMRs within an organizational context. Since this is found to be a present gap in practice, our work makes a contribution here. We propose a novel approach for identifying and managing IMRs based on combining three core technologies: common sense knowledge, text mining and ontology. We claim that discovery and handling of unknown and non-elicited requirements would reduce risks and costs in software development.