To address this challenge, we developed a method for creating models that predict the instability of a user story before it is assigned to a sprint. This method involves applying machine learning techniques over training data automatically extracted from user stories that have already been implemented.
Mar 18, 2022
Firstly, we develop a method to automatically monitor the quality of User Stories. Secondly, we investigate the relationship between User Story quality and ...
The 31st IEEE International Requirements Engineering Conference (RE'23) will host a journal-first track, allowing authors of selected journal-first papers ...
Software and installation requirement for research paper "An Impact-Driven Approach to Predict User Stories Instability". Includes detailed README with po.
Jan 5, 2022 · Software and installation requirement for research paper "An Impact-Driven Approach to Predict User Stories Instability".
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Fingerprint. Dive into the research topics of 'An impact-driven approach to predict user stories instability'. Together they form a unique fingerprint.
Nevertheless, there are no widely accepted quantitative metrics to evaluate user stories. We propose a novel metric to evaluate user stories called instability, ...
Guenther Ruhe. Original Article Open access 17 March 2022 Pages: 211 - 230. An impact-driven approach to predict user stories instability. Yarden Levy; Roni ...
Using user stories estimated based on risk and complexity that are ruthlessly prioritized using that that information is the best way we've seen to ...
Missing: approach instability.
In this work, we introduce a framework for tagging user stories to construct labeled datasets for user stories for various tasks.