Technique to Baseline QE Artefact Generation Aligned to Quality Metrics

2025-11-18

AI Summary

This study addresses the uncontrolled quality of quality engineering (QE) artifacts—such as requirements specifications, test cases, and Behavior-Driven Development (BDD) scenarios—automatically generated by large language models (LLMs). We propose an iterative optimization framework integrating forward generation, backward generation, and rubric-guided scoring to enhance artifact quality along four dimensions: clarity, completeness, consistency, and testability. Our approach enables automated, quantitative, and reproducible quality assessment and improvement. Evaluated across 12 real-world projects, the method significantly improves output stability: it preserves high quality under high-quality inputs and substantially outperforms baselines under low-quality inputs. The core contribution is the first integration of backward generation with structured rubric-based guidance, establishing a closed-loop, artifact-centric quality enhancement paradigm for QE.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Natural Language Processing: Generation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

Large Language Models (LLMs) are transforming Quality Engineering (QE) by automating the generation of artefacts such as requirements, test cases, and Behavior Driven Development (BDD) scenarios. However, ensuring the quality of these outputs remains a challenge. This paper presents a systematic technique to baseline and evaluate QE artefacts using quantifiable metrics. The approach combines LLM-driven generation, reverse generation , and iterative refinement guided by rubrics technique for clarity, completeness, consistency, and testability. Experimental results across 12 projects show that reverse-generated artefacts can outperform low-quality inputs and maintain high standards when inputs are strong. The framework enables scalable, reliable QE artefact validation, bridging automation with accountability.

Problem

• Establishing baselines for automated QE artefact quality evaluation
• Ensuring generated requirements and test cases meet quality metrics
• Validating LLM outputs through reverse generation and iterative refinement

Innovation

• Systematic baselining technique for QE artefacts
• Combines LLM generation with reverse generation
• Iterative refinement guided by quantifiable quality metrics