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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
eGEN: An Energy-saving Modeling Language and Code Generat...
Kowndinya Boyalakuntla, Marimuthu C, Sridhar Chimalakonda, Chand · 2022-04-08 · via cs.SE updates on arXiv.org

The demand for reducing the energy consumption of location-based applications has increased in recent years. The abnormal battery-draining behavior of GPS makes it difficult for the developers to decide on battery optimization during the development phase directly. It will reduce the burden on developers if battery-saving strategies are considered early, and relevant battery-aware code is generated from the design phase artifacts. Therefore, we aim to develop tool support, eGEN, to specify and create native location-based mobile apps. eGEN consists of Domain-specific Modeling Language (DSML) and a code generator for location-sensing. It is developed using Xtext and Xtend as an Eclipse plug-in, and currently, it supports native Android apps. eGEN is evaluated through controlled experiments by instrumenting the generated code in five location-based open-source Android applications. The experimental results show 4.35 minutes of average GPS reduction per hour and 188 mA of average reduction in battery consumption while showing only 97 meters degrade in location accuracy over 3 kilometers of a cycling path. Hence, we believe that code generated by eGEN would help developers to balance between energy and accuracy requirements of location-based applications. The source code, documentation, tool demo video, and tool installation video are available at https://github.com/Kowndinya2000/egen.