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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
Automated Refactoring of Legacy JavaScript Code to ES6 Mo...
Katerina Paltoglou, Vassilis E. Zafeiris, N. A. Diamantidis, E. · 2021-07-21 · via cs.SE updates on arXiv.org

The JavaScript language did not specify, until ECMAScript 6 (ES6), native features for streamlining encapsulation and modularity. Developer community filled the gap with a proliferation of design patterns and module formats, with impact on code reusability, portability and complexity of build configurations. This work studies the automated refactoring of legacy ES5 code to ES6 modules with fine-grained reuse of module contents through the named import/export language constructs. The focus is on reducing the coupling of refactored modules through destructuring exported module objects to fine-grained module features and enhancing module dependencies by leveraging the ES6 syntax. We employ static analysis to construct a model of a JavaScript project, the Module Dependence Graph (MDG), that represents modules and their dependencies. On the basis of MDG we specify the refactoring procedure for module migration to ES6. A prototype implementation has been empirically evaluated on 19 open source projects. Results highlight the relevance of the refactoring with a developer intent for fine-grained reuse. The analysis of refactored code shows an increase in the number of reusable elements per project and reduction in the coupling of refactored modules. The soundness of the refactoring is empirically validated through code inspection and execution of projects' test suites.