Jul 2026· Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Courses· 0 citations· 1 references
TL;DR
This course teaches a repeatable, production-oriented method for debugging OpenUSD composition issues by focusing on how artists, technical directors, and pipeline developers can investigate real failures by tracing symptoms back to authored opinions, layer stacks, asset resolution, references, variants, edit targets, and render-facing overrides.
Abstract
This course teaches a repeatable, production-oriented method for debugging OpenUSD composition issues. Rather than attempting to cover every USD feature, the course focuses on how artists, technical directors, and pipeline developers can investigate real failures by tracing symptoms back to authored opinions, layer stacks, asset resolution, references, variants, edit targets, and render-facing overrides. The course is structured as a hands-on workshop that alternates between conceptual slides and guided Maya exercises. Participants first build a minimal mental model of stages, prims, layers, opinions, composition, and strongest opinion resolution. They then apply that model to a sequence of production-style case files: composed asset construction, broken-looking assets, missing scenes, references with no visible result, variant configuration failures, edit target mistakes, unexpected shot changes, and render overrides. The central pedagogy is the USD Detective Model: observe the problem, find the prim, inspect composition, identify the winning opinion, trace the source layer, and fix the correct layer. By the end of the course, attendees will be able to distinguish resolver failures from composition failures, recognize when USD is behaving correctly but the configuration is incomplete, and use edit-forwarding concepts to prevent bad opinions before they enter the pipeline.
DebugTracker is a Visual Studio Code extension that records lightweight debugging-process evidence for classroom tasks that captures process evidence through standard VS Code mechanisms rather than language-specific tooling, although debugger evidence depends on the relevant VS Code language extension.
Jiatong Liu, Xuesong Yao, Zehua Zhang et al.· 0 citations
This paper identifies patch verbosity as a major yet overlooked concern in LLM-based APR and proposes RECAP, a lightweight, plug-and-play adapter that attaches to existing repair frameworks after generation that achieves a substantially better size-correctness tradeoff.
Wenqiang Luo, J. Keung, Xiaoyu Shi et al.· 0 citations
Self-supervised automated program repair (APR) leverages project-specific perturbations to generate training data and uses test execution diagnostics to guide patch generation. In practice, however, diagnostics are heterogeneous (e.g., exception messages, stack traces, assertion diffs, and dynamic execution signals) and must fit within a strict context budget. Naive concatenation either truncates critical evidence or amplifies noisy artifacts, especially for deep bugs where the failure symptom is far from the root cause. We present TraceStructRepair, a diagnostic structuring and budgeting approach for execution-aware self-supervised APR. TraceStructRepair (1) extracts a compact set of execution diagnostics from a single failing test, including exception type and message, stack trace frames, assertion diffs, and optionally dynamically loaded classes; (2) normalizes and ranks diagnostic elements to reduce redundancy and framework noise; and (3) assembles a field-aware representation under a fixed token budget with per-field caps and lexicographic priority rules. We implement TraceStructRepair on top of the SelfAPR pipeline and evaluate it on Defects4J using a project-wise heldout protocol. Beyond end-to-end repair outcomes, we analyze robustness under noisy fault localization and component ablations. We release artifacts to facilitate replication and future work on execution-aware, budget-constrained APR.
Pan Lu, Dongcheng Li, W. E. Wong· Annual International Compute...· 0 citations
This paper formalizes structural coherence as consistency invariants over graph representations of repository artifacts, including import, call, dependency, configuration, schema, resource, control-flow, and routing graphs, and introduces an eight-category failure taxonomy distinguishing defects specific to LLM generation from those merely amplified by it.
WasmMend is presented, the first system to automatically repair Native-Wasm functional discrepancies and demonstrates the value of divergence-guided reasoning for cross-platform repair.
Programming error messages are critical for software development, yet they remain difficult for novice programmers to interpret. While Large Language Models (LLMs) can rewrite these errors into clearer explanations, it remains unclear whether increased readability improves objective debugging performance or how explanation styles should align with programmer skill. We present a multi-stage crowdsourced study N=103 evaluating skill-targeted, LLM-generated Python error messages. Using a custom proficiency assessment, we categorized participants by skill level and tested standard interpreter messages against two LLM-generated styles: pragmatic (action-oriented) and contingent (scaffolded explanations). We measured both objective debugging metrics (fix rate, attempts, time-to-fix) and subjective perceptions (readability, cognitive load, tone). Our results show that while LLM-rewritten messages significantly improved subjective evaluations, with pragmatic messages rated as clearer and less cognitively demanding, these perceived gains did not translate into statistically significant improvements in objective debugging performance. This highlights a critical human-AI complementarity gap: explanations that feel better to users do not necessarily make them more effective debuggers. We discuss design implications for adaptive AI feedback systems, arguing that future tools should pivot from static skill-targeted rewriting toward dynamic adjustments based on a user's real-time repair trajectory.
Alexandru-Radu Moraru, Shreyan Biswas, U. Gadiraju· 0 citations