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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
LaForge: Always-Correct and Fast Incremental Builds from ...
2021-08-28 · via cs.SE updates on arXiv.org

Developers rely on build systems to generate software from code. At a minimum, a build system should produce build targets from a clean copy of the code. However, developers rarely work from clean checkouts. Instead, they rebuild software repeatedly, sometimes hundreds of times a day. To keep rebuilds fast, build systems run incrementally, executing commands only when built state cannot be reused. Existing tools like make present users with a tradeoff. Simple build specifications are easy to write, but limit incremental work. More complex build specifications produce faster incremental builds, but writing them is labor-intensive and error-prone. This work shows that no such tradeoff is necessary; build specifications can be both simple and fast. We introduce LaForge, a novel build tool that eliminates the need to specify dependencies or incremental build steps. LaForge builds are easy to specify; developers write a simple script that runs a full build. Even a single command like gcc src/*.c will suffice. LaForge traces the execution of the build and generates a transcript in the TraceIR language. On later builds, LaForge evaluates the TraceIR transcript to detect changes and perform an efficient incremental rebuild that automatically captures all build dependencies. We evaluate LaForge by building 14 software packages, including LLVM and memcached. Our results show that LaForge automatically generates efficient builds from simple build specifications. Full builds with LaForge have a median overhead of 16.1% compared to a project's default full build. LaForge's incremental builds consistently run fewer commands, and most take less than 3.08s longer than manually-specified incremental builds. Finally, LaForge is always correct.