Feature Request Analysis and Processing: Tasks, Techniques, and Trends

• URL: http://arxiv.org/abs/2508.12436v1
• Date: Sun, 17 Aug 2025 17:09:16 GMT
• Title: Feature Request Analysis and Processing: Tasks, Techniques, and Trends
• Abstract summary: Satisfying users' demands can benefit the product from both competitiveness and user satisfaction.<n>We select and analyze 131 primary studies using descriptive statistics and qualitative analysis methods.<n>We identify several key challenges and opportunities, such as: (1) ensuring the quality of feature requests, (2) improving their specification and validation, and (3) developing high-quality benchmarks for large language model-driven tasks.
• Score: 12.002693348057738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature requests are proposed by users to request new features or enhancements of existing features of software products, which represent users' wishes and demands. Satisfying users' demands can benefit the product from both competitiveness and user satisfaction. Feature requests have seen a rise in interest in the past few years and the amount of research has been growing. However, the diversity in the research topics suggests the need for their collective analysis to identify the challenges and opportunities so as to promote new advances in the future. In this work, following a defined process and a search protocol, we provide a systematic overview of the research area by searching and categorizing relevant studies. We select and analyze 131 primary studies using descriptive statistics and qualitative analysis methods. We classify the studies into different topics and group them from the perspective of requirements engineering activities. We investigate open tools as well as datasets for future research. In addition, we identify several key challenges and opportunities, such as: (1) ensuring the quality of feature requests, (2) improving their specification and validation, and (3) developing high-quality benchmarks for large language model-driven tasks.

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