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cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Understanding Differences among Executions with Variation...
Jens Meinicke, Chu-Pan Wong, Christian Kästner, Gunter Saake · 2018-07-11 · via cs.SE updates on arXiv.org

One of the main challenges of debugging is to understand why the program fails for certain inputs but succeeds for others. This becomes especially difficult if the fault is caused by an interaction of multiple inputs. To debug such interaction faults, it is necessary to understand the individual effect of the input, how these inputs interact and how these interactions cause the fault. The differences between two execution traces can explain why one input behaves differently than the other. We propose to compare execution traces of all input options to derive explanations of the behavior of all options and interactions among them. To make the relevant information stand out, we represent them as variational traces that concisely represents control-flow and data-flow differences among multiple concrete traces. While variational traces can be obtained from brute-force execution of all relevant inputs, we use variational execution to scale the generation of variational traces to the exponential space of possible inputs. We further provide an Eclipse plugin Varviz that enables users to use variational traces for debugging and navigation. In a user study, we show that users of variational traces are more than twice as fast to finish debugging tasks than users of the standard Eclipse debugger. We further show that variational traces can be scaled to programs with many options.