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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
EasyView: Bringing Performance Profiles into Integrated D...
Qidong Zhao, Milind Chabbi, Xu Liu · 2023-12-27 · via cs.SE updates on arXiv.org

Dynamic program analysis (also known as profiling) is well-known for its powerful capabilities of identifying performance inefficiencies in software packages. Although a large number of dynamic program analysis techniques are developed in academia and industry, very few of them are widely used by software developers in their regular software developing activities. There are three major reasons. First, the dynamic analysis tools (also known as profilers) are disjoint from the coding environments such as IDEs and editors; frequently switching focus between them significantly complicates the entire cycle of software development. Second, mastering various tools to interpret their analysis results requires substantial efforts; even worse, many tools have their own design of graphical user interfaces (GUI) for data presentation, which steepens the learning curves. Third, most existing tools expose few interfaces to support user-defined analysis, which makes the tools less customizable to fulfill diverse user demands. We develop EasyView, a general solution to integrate the interpretation and visualization of various profiling results in the coding environments, which bridges software developers with profilers to provide easy and intuitive dynamic analysis during the code development cycle. The novelty of EasyView is three-fold. First, we develop a generic data format, which enables EasyView to support mainstream profilers for different languages. Second, we develop a set of customizable schemes to analyze and visualize the profiles in intuitive ways. Third, we tightly integrate EasyView with popular coding environments, such as Microsoft Visual Studio Code, with easy code exploration and user interaction. Our evaluation shows that EasyView is able to support various profilers for different languages and provide unique insights into performance inefficiencies in different domains.