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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 Peer Review of Software Engineering Papers
Neil A. Ernst, Jeffrey C. Carver, Daniel Mendez, Marco Torchiano · 2020-09-03 · via cs.SE updates on arXiv.org

Peer review is a key activity intended to preserve the quality and integrity of scientific publications. However, in practice it is far from perfect. We aim at understanding how reviewers, including those who have won awards for reviewing, perform their reviews of software engineering papers to identify both what makes a good reviewing approach and what makes a good paper. We first conducted a series of in-person interviews with well-respected reviewers in the software engineering field. Then, we used the results of those interviews to develop a questionnaire used in an online survey and sent out to reviewers from well-respected venues covering a number of software engineering disciplines, some of whom had won awards for their reviewing efforts. We analyzed the responses from the interviews and from 175 reviewers who completed the online survey (including both reviewers who had won awards and those who had not). We report on several descriptive results, including: 45% of award-winners are reviewing 20+ conference papers a year, while 28% of non-award winners conduct that many. 88% of reviewers are taking more than two hours on journal reviews. We also report on qualitative results. To write a good review, the important criteria were it should be factual and helpful, ranked above others such as being detailed or kind. The most important features of papers that result in positive reviews are clear and supported validation, an interesting problem, and novelty. Conversely, negative reviews tend to result from papers that have a mismatch between the method and the claims and from those with overly grandiose claims. The main recommendation for authors is to make the contribution of the work very clear in their paper. In addition, reviewers viewed data availability and its consistency as being important.