Improving agile requirements: the Quality User Story framework and tool

Abstract

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.

Extracting conceptual models from user stories with Visual Narrator

“…The premise of our research is the high adoption yet low quality of user stories in industry [32,37,38]. To improve this situation, we focus on fostering deeper understanding of user stories by creating tool-assisted techniques that support practitioners in creating and communicating about high-quality user stories [36,39].…”

“…Although the ends has a similar structure to the means in this example, this is not always the case [37]: there are at least three main purposes for having the ends: (1) clarifying the means, (2) referencing another user story, or (3) introducing a qualitative requirement. These functions can be combined for a single user story.…”

“…Based on previous work [15,37,66], we define a generic representation of user stories-see the UML class diagram in Fig. 1-that dissects a user story into its constituents.…”

“…Prior to that, we introduced a conceptual model and an NLP-enabled tool for assisting users in writing high-quality user stories [36] that obtained promising results [37]. In this paper, we extend the work in [53] by making two main contributions:…”

Which Requirements Artifact Quality Defects are Automatically Detectable? A Case Study

“…[7], [8], [9], [10]) or cloning [11]. However, it is still an open question to what degree quality defects can be detected automatically or require human expertise (i.e.…”