SPARC: Scenario Planning and Reasoning for Automated C Unit Test Generation

2026-02-18 · Jaid Monwar Chowdhury, Chi-An Fu, Reyhaneh Jabbarvand arxiv

Automated unit test generation for C remains a formidable challenge due to the semantic gap between high-level program intent and the rigid syntactic constraints of pointer arithmetic and manual memory management. While Large Language Models (LLMs) exhibit strong generative capabilities, direct intent-to-code synthesis frequently suffers from the leap-to-code failure mode, where models prematurely emit code without grounding in program structure, constraints, and semantics. This will result in non-compilable tests, hallucinated function signatures, low branch coverage, and semantically irrelevant assertions that cannot properly capture bugs. We introduce SPARC, a neuro-symbolic, scenario-based framework that bridges this gap through four stages: (1) Control Flow Graph (CFG) analysis, (2) an Operation Map that grounds LLM reasoning in validated utility helpers, (3) Path-targeted test synthesis, and (4) an iterative, self-correction validation loop using compiler and runtime feedback. We evaluate SPARC 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 on complex subjects. SPARC retains 94.3% of tests through iterative repair and produces code with significantly higher developer-rated readability and maintainability. By aligning LLM reasoning with program structure, SPARC provides a scalable path for industrial-grade testing of legacy C codebases.

📄 PDF Abstract BibTeX arXiv:2602.16671

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SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale

2026-06-11 · Nils Blank, Paul Mattes, Maximilian Xiling Li, Jakub Suliga 외 arxiv

This work introduces Spatial Annotations from Robot Demonstrations with Reliability Calibration (SPARC), a risk-aware framework that automatically labels robot demonstrations with structured spatial annotations and assig…

Motion Planning

SPaRC: A Spatial Pathfinding Reasoning Challenge

2025-05-22 · Lars Benedikt Kaesberg, Jan Philip Wahle, Terry Ruas, Bela Gipp

Existing reasoning datasets saturate and fail to test abstract, multi-step problems, especially pathfinding and complex rule constraint satisfaction. We introduce SPaRC (Spatial Pathfinding Reasoning Challenge), a datase…

Spatial Reasoning

SpaRC-AD: A Baseline for Radar-Camera Fusion in End-to-End Autonomous Driving

2025-08-14 · Philipp Wolters, Johannes Gilg, Torben Teepe, Gerhard Rigoll arxiv

End-to-end autonomous driving systems promise stronger performance through unified optimization of perception, motion forecasting, and planning. However, vision-based approaches face fundamental limitations in adverse we…

Multi-Object Tracking Trajectory Prediction Trajectory Planning Collision Avoidance

SPARC: A Multi-Agent System for Electrical Circuit Question Answering

2026-06-05 · Mushtari Sadia, Zhenning Yang, Umme Habiba Lamia, Nishat Shawrin 외 arxiv

Electrical circuit diagram QA tasks require complex mathematical reasoning, which remains challenging for multimodal LLMs. We present SPARC, a multi-agent system that answers questions over circuit diagrams by grounding …

Mathematical Reasoning Question Answering

SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs

2026-02-06 · Niccolo Avogaro, Nayanika Debnath, Li Mi, Thomas Frick 외 arxiv

Despite recent successes, test-time scaling -- i.e., dynamically expanding the token budget during inference as needed -- remains brittle for vision-language models (VLMs). Unstructured visual reasoning chains entangle p…

Reinforcement Learning Visual Reasoning