Automated spectrum-based model fault localization within a search framework

Models play a crucial role in software engineering, guiding development processes and ensuring quality. Extended Finite State Machines (EFSM) effectively model complex systems, but ensuring their correctness is a challenging and critical task. This study aims to improve spectrum-based fault localization accuracy in EFSMs by automating the optimization of risk evaluation formula combinations. We propose a Model Fault Localization Framework (MFLF) that automates the generation, selection, and combination of these formulas. Within this framework, the KWOA-LTR method is developed, employing K-Means clustering to classify formulas based on their fault localization capabilities and the Sum of Squared Errors (SSE) metric to determine the optimal number $K$ of formulas. The Whale Optimization Algorithm (WOA) is then used to select an optimal combination of risk evaluation formulas. Subsequently, a learning-to-rank algorithm constructs a fault localization ranking model that integrates the suspiciousness scores derived from these formulas. Leveraging this trained model, KWOA-LTR efficiently and precisely locates faults. Extensive empirical evaluations on 10 representative benchmark EFSMs demonstrate that KWOA-LTR improves fault localization precision, stability, and overall performance, outperforming existing methods and reducing the manual effort required. The MFLF framework has the potential to support automated spectrum-based fault localization in model-based systems. Moreover, KWOA-LTR within this framework is competitive in terms of fault localization performance. The code is publicly available at https://github.com/Renee0715/GMFLF.