The aim of this thesis is to explore the issue of assumptions made during Requirements Engineering (RE). As the initiating phase of a software development process, RE involves activities which are expected to fulfil the needs of the user. The defects which originate during RE are particularly expensive to rectify when uncovered in the later stages of development. Assumptions made in RE, particularly during requirements analysis, are a significant source of defects and contribute to the total rework cost of the software. Therefore, there is a need to make visible and verify these assumptions in order to reduce the overall development cost. This research examines the adaptation of a standard defect detection technique for revealing assumptions during requirements analysis. This is an extension of the previous literature which largely emphasizes the importance of detecting assumptions in software projects via automated tools. A process model for the research, termed the Exploration of Assumptions in Requirements Engineering (EAiRE) has been constructed by defining assumptions in the context of RE. In support, there was a need for a Taxonomy of Assumptions in Requirements Engineering (TARE) to enhance this investigation. Several important principles for detecting and inserting artificial assumptions are defined and explained. Further, two experimental trials were designed (a Scenario Based Experiment and an Assumptions Seeding Experiment).
|Publication status||Unpublished - 2012|