Enhancing User-Feedback Driven Requirements Prioritization
Aurek Chattopadhyay, Nan Niu, Hui Liu, Jianzhang Zhang

TL;DR
This paper improves requirements prioritization by connecting related requirements using user feedback, enhancing existing methods and demonstrating better performance and additional insights in real-world applications.
Contribution
It introduces a novel approach that leverages interconnected requirements and user feedback to improve search-based prioritization methods.
Findings
Enhanced method outperforms ReFeed in experiments
Interconnected requirements improve search-based solutions
Incorporating requirement links uncovers additional 'requires' relations
Abstract
Context: Requirements prioritization is a challenging problem that is aimed to deliver the most suitable subset from a pool of candidate requirements. The problem is NP-hard when formulated as an optimization problem. Feedback from end users can offer valuable support for software evolution, and ReFeed represents a state-of-the-art in automatically inferring a requirement's priority via quantifiable properties of the feedback messages associated with a candidate requirement. Objectives: In this paper, we enhance ReFeed by shifting the focus of prioritization from treating requirements as independent entities toward interconnecting them. Additionally, we explore if interconnecting requirements provides additional value for search-based solutions. Methods: We leverage user feedback from mobile app store to group requirements into topically coherent clusters. Such interconnectedness, in…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
