Requirements Specification using Qualitative Data Analysis

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Requirements engineering is a key activity in software engineering. Eliciting requirements from stakeholders and domain experts is an error-prone process, which, if done poorly, creates significant problems for the ensuing project (or product). At its heart, in requirements engineering, requirements engineers take qualitative data as input (interviews with stakeholders, workshop notes, collateral) and turn them into a requirements specification. We propose to adapt from the social sciences the use of qualitative data analysis (QDA) methods for eliciting requirements. Used in scientific theory building, QDA methods deliver high completeness and consistency of output. In addition, all output is linked back to the original input in a clear and traceable way. We previously adopted QDA for the creation of a domain model in requirements engineering. In this proposal, we plan to develop an extended method and apply it to the creation and evolution of hierarchical natural language requirements specifications. We expect the method to deliver higher completeness and consistency of requirements than what other methods can currently achieve. In addition, we expect that our approach solves the pre-requirements specification (pre-RS) challenge, which has so far been examined only sparingly. We intend to follow a design science approach in our proposed research: We will first identify current problems, then define objectives for a solution to these problems, design an artifact (here the new method) aimed at achieving this objective, build a tool to evaluate the new method, and finally demonstrate and evaluate our method using the tool in a case study.