RETRACTED ARTICLE: An integrated approach towards automated software requirements elicitation from unstructured documents

Abstract

Enormous amount of text documents that contains requirements expressed in form of unstructured text are obtained in big software development projects and they have to be transformed into structured documents. Collecting requirements from such huge volume of informal text is still a key mission to be achieved. The majority of the software requirements data that are available to software engineers are articulated in form of Natural Language text and there is no formal grammar or specified syntax are followed by the customer for writing requirements i.e. they are highly unstructured. Manual requirements elicitation from these unstructured documents takes lot of time, it is also subject to errors and it is tedious to perform. Any deficiency in the process of gathering the requirements will lead to undesirable consequences that affects the software development. Requirements that are confusing and not complete in any aspect can collapse the software development project itself. We focus on developing an integrated framework towards automated software requirements elicitation from unstructured documents. A formal grammar based technique is developed to tackle the complex sentence structure, regular expression grammar rules that analyzes the linguistic elements in unstructured text are developed and applied to extract the core concepts/entities and relations between entities, the building blocks of any information system development. Another important task that contributes for automation is to build a domain ontology consisting of concepts, their attributes and relation between concepts and axioms, it extends guidance in the semantic labelling of the extracted information constructs. Since it is expensive to have a domain expert to be present all through the stages of software requirements elicitation, domain ontology serves as a background knowledge source. The extracted requirements after semantic association are then presented to the user in form of Object Role Model diagrams to obtain their consent. The framework provides an overall accuracy of 91% in terms of semantic mapping of the extracted requirements.