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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
Blindspots in Python and Java APIs Result in Vulnerable Code
Yuriy Brun, Tian Lin, Jessie Elise Somerville, Elisha Myers, Nat · 2021-03-10 · via cs.SE updates on arXiv.org

Blindspots in APIs can cause software engineers to introduce vulnerabilities, but such blindspots are, unfortunately, common. We study the effect APIs with blindspots have on developers in two languages by replicating an 109-developer, 24-Java-API controlled experiment. Our replication applies to Python and involves 129 new developers and 22 new APIs. We find that using APIs with blindspots statistically significantly reduces the developers' ability to correctly reason about the APIs in both languages, but that the effect is more pronounced for Python. Interestingly, for Java, the effect increased with complexity of the code relying on the API, whereas for Python, the opposite was true. Whether the developers considered API uses to be more difficult, less clear, and less familiar did not have an effect on their ability to correctly reason about them. Developers with better long-term memory recall were more likely to correctly reason about APIs with blindspots, but short-term memory, processing speed, episodic memory, and memory span had no effect. Surprisingly, professional experience and expertice did not improve the developers' ability to reason about APIs with blindspots across both languages, with long-term professionals with many years of experience making mistakes as often as relative novices. Finally, personality traits did not significantly affect the Python developers' ability to reason about APIs with blindspots, but less extraverted and more open developers were better at reasoning about Java APIs with blindspots. Overall, our findings suggest that blindspots in APIs are a serious problem across languages, and that experience and education alone do not overcome that problem, suggesting that tools are needed to help developers recognize blindspots in APIs as they write code that uses those APIs.