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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
Evidence-Based Comparison of Modularity Support Between J...
Arlindo Lima, Miguel Goulão, Miguel Pessoa Monteiro · 2011-09-10 · via cs.SE updates on arXiv.org

Background: Aspect-oriented programming (AOP) is an emerging programming paradigm whose focus is about improving modularity, with an emphasis on the modularization of crosscutting concerns. Objective: The goal of this paper is to assess the extent to which an AOP language -ObjectTeams/Java (OT/J) -improves the modularity of a software system. This improvement has been claimed but, to the best of our knowledge, this paper is the first attempting to present quantitative evidence of it. Method: We compare functionally-equivalent implementations of the Gang-of-Four design patterns, developed in Java and OT/J, using software metrics. Results: The results of our comparison support the modularity improvement claims made in the literature. For six of the seven metrics used, the OT/J versions of the patterns obtained significantly better results. Limitations: This work uses a set of metrics originally defined for object-oriented (OO) systems. It may be the case that the metrics are biased, in that they were created in the context of OO programming (OOP), before the advent of AOP. We consider this comparison a stepping stone as, ultimately, we plan to assess the modularity improvements with paradigm independent metrics, which will conceivably eliminate the bias. Each individual example from the sample used in this paper is small. In future, we plan to replicate this experiment using larger systems, where the benefits of AOP may be more noticeable. Conclusion: This work contributes with evidence to fill gaps in the body of quantitative results supporting alleged benefits to software modularity brought by AOP languages, namely OT/J.