A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirements
arXiv:2607.04436v1 Announce Type: cross Abstract: Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development. However, the inherent ambiguity in NLRs can pose significant challenges. In particular, some requirements may be misinterpreted due to varying contextual knowledge and domain-specific expectations of the stakeholders, a phenomenon known as pragmatic ambiguity. This paper presents an approach for detecting and resolving pragmatic ambiguities in NLRs. The approach leverages retrieval-augmented generation technique
The proliferation of complex software systems and AI-driven development demands more robust methods for requirements engineering, making ambiguity resolution a critical area of focus.
Ambiguity in natural language requirements leads to costly errors, delays, and misaligned software development outcomes, directly impacting project success and resource allocation.
This advancement introduces a more reliable method for automating the detection and resolution of pragmatic ambiguities, potentially streamlining software development lifecycles and improving system quality.
- · Software Development Teams
- · Large Language Model Developers
- · AI-powered Requirements Engineering Tools
- · Companies with Complex Software Projects
- · Manual Requirements Review Processes
- · Projects plagued by frequent requirement changes
- · Traditional systems engineering consultants
Improved clarity and efficiency in drafting and interpreting natural language requirements for software projects.
Reduced incidence of software project failures and cost overruns due to misunderstood specifications.
Accelerated development cycles for complex AI systems and mission-critical software, potentially enabling novel applications and services.
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Read at arXiv cs.AI