User stories are a widely adopted requirements notation in agile development. Yet, user stories are too often poorly written in practice and exhibit inherent quality defects. Triggered by this observation, we propose the Quality User Story (QUS) framework, a set of 13 quality criteria that user story writers should strive to conform to. Based on QUS, we present the Automatic Quality User Story Artisan (AQUSA) software tool. Relying on natural language processing (NLP) techniques, AQUSA detects quality defects and suggest possible remedies. We describe the architecture of AQUSA, its implementation, and we report on an evaluation that analyzes 1023 user stories obtained from 18 software companies. Our tool does not yet reach the ambitious 100 % recall that Daniel Berry and colleagues require NLP tools for RE to achieve. However, we obtain promising results and we identify some improvements that will substantially improve recall and precision.
As organizations change, their information systems can evolve from simple systems to complex systems, which are hard to understand, and therefore hard to maintain or extend. Process mining can help organizations in trying to understand the information systems by analyzing the system.In this paper we propose a methodology to perform process diagnostics, based on process mining. Given an event log of an information system within an organization, process diagnostics gives a broad overview of the organization's process(es) within a short period of time. In the process diagnostics methodology, several perspectives of the process are highlighted. The outcome covers the control flow perspective, the performance perspective and the organizational perspective. We used the methodology on a case study for a Dutch governmental organization.
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