A framework for identifying and analyzing non-functional requirements from text

Abstract

Early identification of Non-Functional Requirements (NFRs) is important as this has direct bearing on the design and architecture of the system. NFRs form the basis for architects to create the technical architecture of the system which acts as the scaffolding in which the functionality of the same is delivered. Failure to identify and analyze NFRs early-on can result in unclassified, incomplete or conflicting NFRs, and this typically results in costly rework in later stages of the software development. In practice, this activity is primarily done manually. In this paper, we present a framework to automatically detect and classify non-functional requirements from textual natural language requirements. Our approach to identify NFRs is based on extracting multiple features by parsing the natural language requirement whereby the presence of a certain combination of and relationship among the features uniquely identifies the requirement as an NFR of a particular category. These features are specified as pattern based rules which can be specified in a human readable language through the use of a domain specific language that we have defined. This enables great ease and flexibility in creating and extending rules. Our approach has been implemented as a prototype tool and here we also present the results of applying our approach on a publicly available requirement corpus.