Abstract
Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and genera-ting a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on pyLDAvis,we initially classify"I want to"phrases within user stories.Sub-sequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted rank-ing function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2 966 crowd-sourced user sto-ries related to smart home systems.
基金项目
National Natural Science Foundation of China(71690233)
National Natural Science Foundation of China(71901214)
NETL
NSTL
维普
万方数据