Technical Summary: AutoRE-NS

Problem Statement

Modern industrial software systems face an exponential escalation in requirements volume and complexity, rendering classical manual engineering processes a bottleneck. While existing automated solutions exist, they are predominantly limited to syntactic analysis and basic consistency validation. These traditional tools fail to provide the deep semantic understanding necessary for advanced conflict detection, quality optimization, and contextual analysis, often resulting in rigid rule-based systems or "black-box" machine learning models lacking explainability.

Methodology

The paper proposes AutoRE-NS, a flexible, automated framework designed to enhance requirement quality and assess complexity. The system employs a neuro-symbolic architecture integrated with reinforcement learning, structured into three distinct layers:

1. Neuro-Perception Layer (Semantic Analysis):

   • Utilizes pretrained transformer models tuned specifically for requirements engineering vocabulary and domain-dependent language.
   • Performs contextual embedding, requirement encoding, classification, relation extraction, and quality scoring.
   • Translates unstructured natural language requirements into structured semantic representations.

2. Symbolic Reasoning Layer (Formal Validation):

   • Employs a knowledge base incorporating requirements engineering best practices, domain constraints, and quality heuristics.
   • Executes a verification engine that checks for consistency, completeness, and feasibility.
   • Generates a comprehensive state vector ($S$) representing normalized quality metrics (e.g., consistency, ambiguity, testability).

3. Adaptive Policy Layer (Optimization):

   • Implements a Markov Decision Process (MDP) to optimize requirements selection and refinement.
   • Powered by a Deep Q-Network (DQN) that learns optimal policies through interaction with a simulated project environment.
   • Dynamically adapts to resolve conflicts and eliminate redundancy while maintaining human oversight.

The framework is designed to be Explainable AI (XAI), providing human-understandable justifications for its decisions, thereby bridging the gap between automated efficiency and engineering trust.

Key Contributions

The paper outlines four primary contributions:

1. Neuro-Symbolic Architecture: A novel three-layer framework for comprehensive requirements analysis that combines neural pattern recognition with symbolic reasoning.
2. Adaptive Optimization: The implementation of deep reinforcement learning to adaptively optimize requirements, moving beyond static rule sets.
3. Explainability: A design that ensures all automated assessments and optimizations are accompanied by transparent, human-readable justifications.
4. Empirical Validation: Rigorous testing conducted on large-scale industrial datasets to demonstrate practical feasibility.

Experimental Results

The framework was evaluated on a dataset of 2,548 requirements spanning six industrial domains (Healthcare, Automotive, Embedded Systems, E-commerce, Finance, and Telecommunications).

Efficiency and Reduction:

• Requirement Volume: Achieved an 87.2% reduction in total requirements (from 2,548 to 326) by eliminating redundancy while preserving semantic content.
• Conflict Resolution: Resolved 86.6% of critical conflicts and 83.1% of moderate conflicts. (Note: The abstract mentions a general figure of 83.5% for conflicts resolved, while Table 3 specifies 83.1% for moderate conflicts specifically).
• Duplication Elimination: Eliminated 97.0% of exact duplicates and 94.4% of semantic duplicates.
• Processing Time: Reduced average processing time from 52.7 hours to 2.4 hours (a 95.4% improvement).

Quality Metric Improvements:
Statistical analysis (p < 0.001 for most metrics) showed significant improvements in quality dimensions:

• Ambiguity: Reduced by 38.4%.
• Consistency: Improved by 25.7%.
• Verifiability: Improved by 30.8%.
• Traceability: Improved by 26.8%.
• Completeness: Increased by 14.2%.
• Atomicity: Increased by 9.8%.

Comparative Performance:
AutoRE-NS outperformed baseline approaches (REQANALYZER, SMART-RE, Rule-based Systems) in conflict resolution (86.6% vs. 48.3–61.5%) and duplication detection (95.7% vs. 58.9–76.8%). While manual review showed slightly higher conflict resolution (94.2%), AutoRE-NS offered a significant advantage in processing speed and scalability.

Significance and Claims

The paper claims that AutoRE-NS represents a significant stride in automating requirements engineering, particularly for safety-oriented, compliance-focused, and complex industrial systems where requirement quality is critical to project success.

The authors assert that the framework successfully addresses the bottleneck of exponential requirements growth by providing a robust, explainable solution that balances automation with human oversight. The extensive validation on industrial-scale datasets demonstrates the approach's practical reliability. The paper concludes that while the current results are promising, future work should focus on enhancing cross-domain adaptation, real-time human-AI integration, and deeper integration with development tooling.

Note: The authors acknowledge threats to validity, noting that while internal validity was strengthened by code reviews and hyperparameter optimization, and external validity was supported by multi-domain testing, further longitudinal studies and broader domain ranges are needed to fully generalize the findings.