Reducing misunderstanding of software requirements by conceptualization of mental models using pathfinder networks

Understanding and communicating user requirements in a software requirement analysis effort is very important. Misunderstandings of user requirements between stakeholders will cause problems in terms of satisfying their needs, reduction of defects, cost and schedule during the software development process. This dissertation presents a new technique that has the ability to represent the mental models of the user, developers, project managers and sponsors (collectively referred to as “stakeholders”) as network representations. The requirements are modeled as nodes and the perception of stakeholders is modeled as the interrelationships (links) among the requirements. The requirements are first extracted from a requirements document. The requirements are then categorized into related groups as perceived by each stakeholder. The relatedness (proximity) data collected from the categories is then fed into the Pathfinder generation program that results in the generation of pathfinder networks (PFNETs). The PFNETs of stakeholders are then compared for similarities/dissimilarities using a graph similarity metric referred to as a correlation coefficient. During preliminary research work, this technique was applied to multiple student projects with real customers at Mississippi State University (MSU), and to a project at NORTEL, Dallas, Texas with encouraging results. This research was successful in identifying duplicate, ambiguous and misunderstood requirements. The next step was to validate this technique on small-scale and medium scale projects in an industrial setting. During the summer of 2003, the National Science Foundation (NSF) and AmerInd Inc. jointly sponsored a collaborative industry-university research effort to validate the proposed technique. It was found that this technique is easy to apply and useful to gauge an overall understanding of requirements and identify potentially misunderstood requirements for small and medium scale projects. This technique scaled well from a small-scale project with two stakeholders to a medium-scale project with a little over one hundred requirements and six stakeholders. The correlations helped focus discussions on the requirements that were potentially misunderstood among stakeholders. Duplicate, misunderstood and ambiguous requirements were identified during the facilitation sessions. We also present a new technique that applies information theory-based software metrics to measure consensus about requirements among stakeholders.