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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
Edit Transactions: Dynamically Scoped Change Sets for Con...
2017-03-31 · via cs.SE updates on arXiv.org

Live programming environments enable programmers to edit a running program and obtain immediate feedback on each individual change. The liveness quality is valued by programmers to help work in small steps and continuously add or correct small functionality while maintaining the impression of a direct connection between each edit and its manifestation at run-time. Such immediacy may conflict with the desire to perform a combined set of intermediate steps, such as a refactoring, without immediately taking effect after each individual edit. This becomes important when an incomplete sequence of small-scale changes can easily break the running program. State-of-the-art solutions focus on retroactive recovery mechanisms, such as debugging or version control. In contrast, we propose a proactive approach: Multiple individual changes to the program are collected in an Edit Transaction, which can be made effective if deemed complete. Upon activation, the combined steps become visible together. Edit Transactions are capable of dynamic scoping, allowing a set of changes to be tested in isolation before being extended to the running application. This enables a live programming workflow with full control over change granularity, immediate feedback on tests, delayed effect on the running application, and coarse-grained undos. We present an implementation of Edit Transactions along with Edit-Transaction-aware tools in Squeak/Smalltalk. We asses this implementation by conducting a case study with and without the new tool support, comparing programming activities, errors, and detours for implementing new functionality in a running simulation. We conclude that workflows using Edit Transactions have the potential to increase confidence in a change, reduce potential for run-time errors, and eventually make live programming more predictable and engaging.