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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
Using Relational Problems to Teach Property-Based Testing
John Wrenn, Tim Nelson, Shriram Krishnamurthi · 2020-10-30 · via cs.SE updates on arXiv.org

Context: The success of QuickCheck has led to the development of property-based testing (PBT) libraries for many languages and the process is getting increasing attention. However, unlike regular testing, PBT is not widespread in collegiate curricula. Furthermore, the value of PBT is not limited to software testing. The growing use of formal methods in, and the growth of software synthesis, all create demand for techniques to train students and developers in the art of specification writing. We posit that PBT forms a strong bridge between testing and the act of specification: it's a form of testing where the tester is actually writing abstract specifications. Inquiry: Even well-informed technologists mention the difficulty of finding good motivating examples for its use. We take steps to fill this lacuna. Approach & Knowledge: We find that the use of "relational" problems -- those for which an input may admit multiple valid outputs -- easily motivates the use of PBT. We also notice that such problems are readily available in the computer science pantheon of problems (e.g., many graph and sorting algorithms). We have been using these for some years now to teach PBT in collegiate courses. Grounding: In this paper, we describe the problems we use and report on students? completion of them. We believe the problems overcome some of the motivation issues described above. We also show that students can do quite well at PBT for these problems, suggesting that the topic is well within their reach. In the process, we introduce a simple method to evaluate the accuracy of their specifications, and use it to characterize their common mistakes. Importance: Based on our findings, we believe that relational problems are an underutilized motivating example for PBT. We hope this paper initiates a catalog of such problems for educators (and developers) to use, and also provides a concrete (though by no means exclusive) method to analyze the quality of PBT.