Model-Based Diagnosis with Multiple Observations: A Unified Approach for C Software and Boolean Circuits

• URL: http://arxiv.org/abs/2512.02898v1
• Date: Tue, 02 Dec 2025 16:04:51 GMT
• Title: Model-Based Diagnosis with Multiple Observations: A Unified Approach for C Software and Boolean Circuits
• Authors: Pedro Orvalho, Marta Kwiatkowska, Mikoláš Janota, Vasco Manquinho,
• Abstract summary: This paper introduces CFaults, a novel fault localisation tool for C software and Boolean circuits with multiple faults.<n>CFaults leverages Model-Based Diagnosis (MBD) with multiple observations and aggregates all failing test cases into a unified Satisfiability (MaxSAT) formula.<n> Experimental results on three benchmark sets, two of C programs, TCAS and C-Pack-IPAs, and one of Boolean circuits, ISCAS85, show that CFaults is faster at localising faults in C software than other FBFL approaches.
• Score: 13.450835224477109
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Debugging is one of the most time-consuming and expensive tasks in software development and circuit design. Several formula-based fault localisation (FBFL) methods have been proposed, but they fail to guarantee a set of diagnoses across all failing tests or may produce redundant diagnoses that are not subset-minimal, particularly for programs/circuits with multiple faults. This paper introduces CFaults, a novel fault localisation tool for C software and Boolean circuits with multiple faults. CFaults leverages Model-Based Diagnosis (MBD) with multiple observations and aggregates all failing test cases into a unified Maximum Satisfiability (MaxSAT) formula. Consequently, our method guarantees consistency across observations and simplifies the fault localisation procedure. Experimental results on three benchmark sets, two of C programs, TCAS and C-Pack-IPAs, and one of Boolean circuits, ISCAS85, show that CFaults is faster at localising faults in C software than other FBFL approaches such as BugAssist, SNIPER, and HSD. On the ISCAS85 benchmark, CFaults is generally slower than HSD; however, it localises faults in only 6% fewer circuits, demonstrating that it remains competitive in this domain. Furthermore, CFaults produces only subset-minimal diagnoses of faulty statements, whereas the other approaches tend to enumerate redundant diagnoses (e.g., BugAssist and SNIPER).

Related papers

• SPARC: Scenario Planning and Reasoning for Automated C Unit Test Generation [1.0010193170880752]
  We introduce a neuro-symbolic, scenario-based framework that bridges the gap between high-level program intent and the rigid syntactic constraints of pointer arithmetic and manual memory management.<n>We evaluate it on 59 real-world and algorithmic subjects, where it outperforms the vanilla prompt generation baseline by 31.36% in line coverage, 26.01% in branch coverage, and 20.78% in mutation score, matching or exceeding the symbolic execution tool KLEE.
  arXiv  Detail & Related papers  (2026-02-18T18:09:03Z)
• ProbeLLM: Automating Principled Diagnosis of LLM Failures [89.44131968886184]
  We propose ProbeLLM, a benchmark-agnostic automated probing framework that elevates weakness discovery from individual failures to structured failure modes.<n>By restricting probing to verifiable test cases and leveraging tool-augmented generation and verification, ProbeLLM grounds failure discovery in reliable evidence.
  arXiv  Detail & Related papers  (2026-02-13T14:33:13Z)
• Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning [53.9713678229744]
  Multi-instance partial-label learning (MIPL) is a weakly supervised framework that addresses the challenges of inexact supervision in both instance and label spaces.<n>Existing MIPL approaches often suffer from poor calibration, undermining reliability.<n>We propose a plug-and-play calibratable disambiguation loss (CDL) that simultaneously improves classification accuracy and calibration performance.
  arXiv  Detail & Related papers  (2025-12-19T16:58:31Z)
• How Many Code and Test Cases Are Enough? Evaluating Test Cases Generation from a Binary-Matrix Perspective [51.30005925128432]
  evaluating test cases automatically generated by Large Language Models (LLMs) is a critical yet challenging task.<n>Existing benchmarks suffer from high computational costs, score inflation, and a bias towards trivial bugs over rare, critical faults.<n>We introduce a framework that formalizes benchmark construction as finding an optimal diagnostic basis in a binary code-test matrix.
  arXiv  Detail & Related papers  (2025-10-09T18:29:24Z)
• Hierarchical Self-Supervised Representation Learning for Depression Detection from Speech [51.14752758616364]
  Speech-based depression detection (SDD) is a promising, non-invasive alternative to traditional clinical assessments.<n>We propose HAREN-CTC, a novel architecture that integrates multi-layer SSL features using cross-attention within a multitask learning framework.<n>The model achieves state-of-the-art macro F1-scores of 0.81 on DAIC-WOZ and 0.82 on MODMA, outperforming prior methods across both evaluation scenarios.
  arXiv  Detail & Related papers  (2025-10-05T09:32:12Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Lightweight Fault Detection Architecture for NTT on FPGA [0.8793721044482612]
  Post-Quantum Cryptographic (PQC) algorithms are mathematically secure and resistant to quantum attacks.<n>They can still leak sensitive information in hardware implementations due to natural faults or intentional fault injections.<n>This research proposes a lightweight, efficient, recomputation-based fault detection module.
  arXiv  Detail & Related papers  (2025-08-05T04:23:50Z)
• Specification-Guided Repair of Arithmetic Errors in Dafny Programs using LLMs [79.74676890436174]
  We present an APR tool for Dafny that uses formal specifications as oracles for fault localization and repair.<n>We localize faults through a series of steps, which include using Hoare logic to determine the state of each statement within the program.<n>Our tool achieves 89.6% fault localization coverage and GPT-4o mini yields the highest repair success rate of 74.18%.
  arXiv  Detail & Related papers  (2025-07-04T15:36:12Z)
• Black-Box Test Code Fault Localization Driven by Large Language Models and Execution Estimation [7.040370156228408]
  We introduce a fully static, LLM-driven approach for system test code fault localization.<n>Our method uses a single failure execution log to estimate the test's execution trace.<n>We evaluate our technique at function, block, and line levels using an industrial dataset of faulty test cases.
  arXiv  Detail & Related papers  (2025-06-23T19:04:51Z)
• CFaults: Model-Based Diagnosis for Fault Localization in C Programs with Multiple Test Cases [2.8853600644505266]
  This paper introduces a novel fault localization approach for C programs with multiple faults.<n>CFaults leverages Model-Based Diagnosis (MBD) with multiple observations and aggregates all failing test cases into a unified MaxSAT formula.<n> Experimental results on two benchmark sets of C programs, TCAS and C-Pack-IPAs, show that CFaults is faster than other FBFL approaches.
  arXiv  Detail & Related papers  (2024-07-12T15:14:49Z)
• SBEST: Spectrum-Based Fault Localization Without Fault-Triggering Tests [17.90798133817018]
  This study investigates the feasibility of using stack traces from crash reports as proxies for fault-triggering tests in Spectrum-Based Fault localization.<n>We propose SBEST, a novel approach that integrates stack trace information with test coverage data to perform fault localization when fault-triggering tests are missing.
  arXiv  Detail & Related papers  (2024-05-01T15:15:52Z)
• Fast and Accurate Error Simulation for CNNs against Soft Errors [64.54260986994163]
  We present a framework for the reliability analysis of Conal Neural Networks (CNNs) via an error simulation engine. These error models are defined based on the corruption patterns of the output of the CNN operators induced by faults. We show that our methodology achieves about 99% accuracy of the fault effects w.r.t. SASSIFI, and a speedup ranging from 44x up to 63x w.r.t.FI, that only implements a limited set of error models.
  arXiv  Detail & Related papers  (2022-06-04T19:45:02Z)
• An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets [68.8204255655161]
  We introduce a divide-and-conquer based diagnosis algorithm (FastDiag) which identifies minimal sets of faulty constraints in an over-constrained problem. We compare FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis.
  arXiv  Detail & Related papers  (2021-02-17T19:55:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.