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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
Dissecting Smart Contract Languages: A Survey
Majd Soud, Gísli Hjálmtýsson, Mohammad Hamdaqa · 2023-10-04 · via cs.SE updates on arXiv.org

Blockchain is a distributed ledger technology that gained popularity for enabling the transformation of cryptocurrency among peers without mediation by a centralized third-party authority. Smart contracts expand the applications of blockchain technology and have played a role in its widespread adoption. Smart contracts are immutable digital programs that are deployed on blockchains to codify agreements between parties. Existing smart contract implementations have faced challenges, including security vulnerabilities, leading to significant losses and concerns. This has stimulated a wave of attempts to improve Smart Contract Languages (SCLs) to overcome implementation challenges and ensure code quality, producing many languages with diverse features. Scholars have made some attempts to classify SCLs and clarify the process of selecting an SCL, but to the best of our knowledge, no comprehensive survey of existing SCLs has been published. Our work surpasses earlier efforts by evaluating a significantly larger set of SCLs, in greater depth, to ease the process of SCL selection for blockchain research and implementation. In this paper, we (1) propose a robust framework for comparing existing SCLs, (2) analyze and discuss 36 SCLs, addressing issues beyond those used to construct the comparison framework, and (3) define new parameters for future research and development of SCLs. The survey provides a guide for those who intend to select or use an SCL to implement smart contracts, develop new SCLs, or add new extensions to the existing SCLs.