Interviews with nine data-intensive programmers identify three cross-cutting debugging challenges that visualization can address via evidence alignment, expectation comparison, and state tracing.
Program slicing
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LEO is a cross-vendor GPU stall root-cause analyzer that traces stalled instructions backward through register and synchronization dependencies, achieving 1.73–1.82x geometric-mean speedups across 21 workloads on NVIDIA, AMD, and Intel GPUs.
An empirical study finds that injecting call/inheritance topology as comments improves LLM code agent localization by 2.2pp, shortens trajectories by 1.6 rounds, and halves run-to-run variance on medium repositories via a deterministic anchoring effect.
citing papers explorer
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Debugging as Evidence-Driven Reasoning: Visualization Opportunities in Data-Intensive Programming
Interviews with nine data-intensive programmers identify three cross-cutting debugging challenges that visualization can address via evidence alignment, expectation comparison, and state tracing.
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LEO: Tracing GPU Stall Root Causes via Cross-Vendor Backward Slicing
LEO is a cross-vendor GPU stall root-cause analyzer that traces stalled instructions backward through register and synchronization dependencies, achieving 1.73–1.82x geometric-mean speedups across 21 workloads on NVIDIA, AMD, and Intel GPUs.
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How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring
An empirical study finds that injecting call/inheritance topology as comments improves LLM code agent localization by 2.2pp, shortens trajectories by 1.6 rounds, and halves run-to-run variance on medium repositories via a deterministic anchoring effect.