Designing a Requirement Mining System

The success of information systems (IS) development strongly depends on the accuracy of the requirements gathered from users and other stakeholders. When developing a new IS, about 80 percent of these requirements are recorded in informal requirements documents (e.g., interview transcripts or discussion forums) using natural language. However, processing the resultant natural language requirements resources is inherently complex and often error prone due to ambiguity, inconsistency, and incompleteness. Thus, even highly qualified requirements engineers often struggle to process large amounts of natural language requirements resources efficiently and effectively. In this paper, we propose a design theory for requirement mining systems (RMSs) based on two design principles: (1) semi-automatic requirement mining and (2) usage of imported and retrieved knowledge. As part of an extensive design project, which led to these principles, we also implemented a prototype based on this design theory (REMINER). It supports requirements engineers in identifying and classifying requirements documented in natural language and allows us to evaluate the artifact’s viability and the conceptual soundness of our design. The results of our evaluation suggest that an RMS based on our proposed design principles can significantly improve recall while maintaining precision levels

A design theory for requirements mining systems

Software requirements are often communicated in unstructured text documents, which need to be analyzed in order to identify and classify individual needs. This process is referred to as requirements mining in the context of this thesis. It is known to be time-consuming and error-prone when performed manually by a requirements engineer. Thus, there is a demand to support requirements mining through information technology. However, little research has been conducted to conceptualize theoretically grounded requirements mining systems and abstract the necessary design knowledge in a theory. Furthermore, existing works scarcely investigate the effect of these artifacts on requirements engineers’ productivity. Consequently in this thesis, the following research question is addressed: How can a system be designed which aims at improving requirements mining productivity over manual discovery? Following a Design Science approach, a design theory is derived consisting of design requirements, design principles and design features. Design requirements are identified based on general knowledge and kernel theories. Subsequently they are related to design principles which are finally mapped to design features of an artifact. The artifact is conceptualized in two design cycles, each resulting in a distinct artifact version and its evaluation. In the first design cycle a simulation is conducted to investigate the interplay of the preliminary design principles. In the second design cycle, the effects of the final design principles on requirements mining productivity are measured in an experiment. The thesis contributes to the design theory body of knowledge by providing a design theory for requirements mining systems. The theory is a contribution to the information systems literature because requirements mining systems represent an important class of design situations that have not been adequately described yet by existing works. From a practical point of view, the study addresses the need of requirements engineers to support their work by information technology and provides vendors of requirements engineering software packages guidelines to improve their products

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band III

Übersicht der Teilkonferenzen Band III • Service Systems Engineering • Sicherheit, Compliance und Verfügbarkeit von Geschäftsprozessen • Smart Services: Kundeninduzierte Kombination komplexer Dienstleistungen • Strategisches IT-Management • Student Track • Telekommunikations- und Internetwirtschaft • Unternehmenssoftware – quo vadis? • Von der Digitalen Fabrik zu Industrie 4.0 – Methoden und Werkzeuge für die Planung und Steuerung von intelligenten Produktions- und Logistiksystemen • Wissensmanagemen