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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
Developer Perspectives on REST API Usability: A Study of ...
Sven Peldszus, Jan Rutenkolk, Marcel Heide, Jan Sollmann, Benjam · 2026-01-23 · via cs.SE updates on arXiv.org

REST is today's most widely used architectural style for providing web-based services. In the age of service-orientation (a.k.a. Software as a Service (SaaS)) APIs have become core business assets and can easily expose hundreds of operations. While well-designed APIs contribute to the commercial success of a service, poorly designed APIs can threaten entire organizations. Recognizing their relevance and value, many guidelines have been proposed for designing usable APIs, similar to design patterns and coding standards. For example, Zalando and Microsoft provide popular REST API guidelines. However, they are often considered as too large and inapplicable, so many companies create and maintain their own guidelines, which is a challenge in itself. In practice, however, developers still struggle to design effective REST APIs. To improve the situation, we need to improve our empirical understanding of adopting, using, and creating REST API guidelines. We present an interview study with 16 REST API experts from industry. We determine the notion of API usability, guideline effectiveness factors, challenges of adopting and designing guidelines, and best practices. We identified eight factors influencing REST API usability, among which the adherence to conventions is the most important one. While guidelines can in fact be an effective means to improve API usability, there is significant resistance from developers against strict guidelines. Guideline size and how it fits with organizational needs are two important factors to consider. REST guidelines also have to grow with the organization, while all stakeholders need to be involved in their development and maintenance. Automated linting provides an opportunity to not only embed compliance enforcement into processes, but also to justify guideline rules with educational explanations.