Improving Requirements Completeness: Automated Assistance through Large Language Models

TL;DR

This paper investigates using BERT, a large language model, to detect incompleteness in natural language requirements by predicting missing content, demonstrating its effectiveness and proposing methods to optimize and filter predictions.

Contribution

It introduces a novel approach utilizing BERT's MLM to identify missing information in requirements and develops techniques to optimize prediction accuracy and reduce noise.

Findings

BERT effectively highlights missing terminology in requirements.

BERT outperforms simpler baseline models in detecting omissions.

A machine learning filter reduces noise and improves prediction quality.

Abstract

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.