The use of logic in identifying and analyzing inconsistency in requirements from multiple stakeholders has been found to be effective in a number of studies. Nonmonotonic logic is a theoretically well-founded formalism that is especially suited for supporting the evolution of requirements. However, direct use of logic for expressing requirements and discussing them with stakeholders poses serious usability problems, since in most cases stakeholders cannot be expected to be fluent with formal logic. In this article, we explore the integration of natural language parsing techniques with default reasoning to overcome these difficulties. We also propose a method for automatically discovering inconsistencies in the requirements from multiple stakeholders, using both theorem-proving and model-checking techniques, and show how to deal with them in a formal manner. These techniques were implemented and tested in a prototype tool calledCARL. The effectiveness of the techniques and of the tool are illustrated by a classic example involving conflicting requirements from multiple stakeholders.

The importance of requirements, which in practice often means natural language requirements, for a successful software project cannot be underestimated. Although requirement analysis has been traditionally reserved to the experience of professionals, there is no reason not to use various automatic techniques to the same end. In this paper we present Circe, a Web-based environment for aiding in natural language requirements gathering, elicitation, selection, and validation and the tools it integrates. These tools have been used in several experiments both in academic and in industrial environments. Among other features, Circe can extract abstractions from natural language texts, build various models of the system described by the requirements, check the validity of such models, and produce functional metric reports. The environment can be easily extended to enhance its natural language recognition power or to add new models and views on them.

Natural language processing (NLP) and requirements engineering (RE) have had a long relationship, yet their combined use isn’t well established in industrial practice. This situation should soon change. The future evolution of the application of NLP technologies in RE can be viewed from four dimensions: discipline, dynamism, domain knowledge, and datasets.