# Improving Requirements Completeness: Automated Assistance through Large Language Models

arXiv: https://arxiv.org/abs/2308.03784 · HTML: https://mlsanity.com/paper/2308.03784/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2023.

## Built on by (most cited first, 10 of 10)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training](https://mlsanity.com/paper/2406.11439/) (2024)
- [Can LLMs Generate User Stories and Assess Their Quality?](https://mlsanity.com/paper/2507.15157/) (2025)
- [ReqInOne: A Large Language Model-Based Agent for Software Requirements Specification Generation](https://mlsanity.com/paper/2508.09648/) (2025)
- [ClarifySTL: An Interactive LLM Agent Framework for STL Transformation through Requirements Clarification](https://mlsanity.com/paper/2605.01209/) (2026)
- [SIExVulTS: Sensitive Information Exposure Vulnerability Detection System using Transformer Models and Static Analysis](https://mlsanity.com/paper/2508.19472/) (2025)
- [LLM assisted web application functional requirements generation: A case study of four popular LLMs over a Mess Management System](https://mlsanity.com/paper/2505.18019/) (2025)
- [Towards an Agentic LLM-based Approach to Requirement Formalization from Unstructured Specifications](https://mlsanity.com/paper/2604.18228/) (2026)

## Abstract (arXiv)

Natural language (NL) is arguably the most prevalent medium for expressing systems and software requirements. Detecting incompleteness in NL requirements is a major challenge. One approach to identify incompleteness is to compare requirements with external sources. Given the rise of large language models (LLMs), an interesting question arises: Are LLMs useful external sources of knowledge for detecting potential incompleteness in NL requirements? This article explores this question by utilizing BERT. Specifically, we employ BERT's masked language model (MLM) to generate contextualized predictions for filling masked slots in requirements. To simulate incompleteness, we withhold content from the requirements and assess BERT's ability to predict terminology that is present in the withheld content but absent in the disclosed content. BERT can produce multiple predictions per mask. Our first contribution is determining the optimal number of predictions per mask, striking a balance between effectively identifying omissions in requirements and mitigating noise present in the predictions. Our second contribution involves designing a machine learning-based filter to post-process BERT's predictions and further reduce noise. We conduct an empirical evaluation using 40 requirements specifications from the PURE dataset. Our findings indicate that: (1) BERT's predictions effectively highlight terminology that is missing from requirements, (2) BERT outperforms simpler baselines in identifying relevant yet missing terminology, and (3) our filter significantly reduces noise in the predictions, enhancing BERT's effectiveness as a tool for completeness checking of requirements.