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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
The Sense of Logging in the Linux Kernel
Keyur Patel, Joao Faccin, Abdelwahab Hamou-Lhadj, Ingrid Nunes · 2022-08-13 · via cs.SE updates on arXiv.org

Logging plays a crucial role in software engineering because it is key to perform various tasks including debugging, performance analysis, and detection of anomalies. Despite the importance of log data, the practice of logging still suffers from the lack of common guidelines and best practices. Recent studies investigated logging in C/C++ and Java open-source systems. In this paper, we complement these studies by conducting the first empirical study on logging practices in the Linux kernel, one of the most elaborate open-source development projects in the computer industry. We analyze 22 Linux releases with a focus on three main aspects: the pervasiveness of logging in Linux, the types of changes made to logging statements, and the rationale behind these changes. Our findings show that logging code accounts for 3.73% of the total source code in the Linux kernel, distributed across 72.36% of Linux files. We also found that the distribution of logging statements across Linux subsystems and their components vary significantly with no apparent reasons, suggesting that developers use different criteria when logging. In addition, we observed a slow decrease in the use of logging-reduction of 9.27% between versions v4.3 and v5.3. The majority of changes in logging code are made to fix language issues, modify log levels, and upgrade logging code to use new logging libraries, with the overall goal of improving the precision and consistency of the log output. Many recommendations are derived from our findings such as the use of static analysis tools to detect log-related issues, the adoption of common writing styles to improve the quality of log messages, the development of conventions to guide developers when selecting log levels, the establishment of review sessions to review logging code, and so on. [...]