Towards More Dependable Specifications: An Empirical Study Exploring the Synergy of Traditional and LLM-Based Repair Approaches

Paper in proceeding, 2025

Declarative specification languages like Alloy are critical for modeling and verifying complex software systems, yet repairing these specifications remains a significant challenge for ensuring software dependability. This study conducts the first comprehensive empirical evaluation comparing traditional systematic repair techniques with emerging Large Language Model (LLM)-based approaches across two established benchmarks, analyzing over 1,900 Alloy specifications. By systematically analyzing repair success rates, ground truth similarity, and repair generation strategies, we reveal nuanced performance characteristics of different repair methodologies. Our findings demonstrate that while traditional tools excel in systematic fault localization and achieving high ground truth similarity, LLM-based techniques-particularly multi-round prompting approaches-offer unique capabilities in addressing complex specification errors, with some hybrid approaches achieving repair rates of up to 85.5%. Critically, we show that integrating traditional fault localization techniques with LLM-based repair strategies can significantly enhance overall repair effectiveness and specification dependability. This research provides a large-scale empirical evaluation of how various Alloy repair techniques work in synergy, offering valuable insights that chart a promising path for future automated specification repair approaches and contribute to the development of more reliable and secure software systems.