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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
AndroLog: Android Instrumentation and Code Coverage Analysis
Jordan Samhi, Andreas Zeller · 2024-04-17 · via cs.SE updates on arXiv.org

Dynamic analysis has emerged as a pivotal technique for testing Android apps, enabling the detection of bugs, malicious code, and vulnerabilities. A key metric in evaluating the efficacy of tools employed by both research and practitioner communities for this purpose is code coverage. Obtaining code coverage typically requires planting probes within apps to gather coverage data during runtime. Due to the general unavailability of source code to analysts, there is a necessity for instrumenting apps to insert these probes in black-box environments. However, the tools available for such instrumentation are limited in their reliability and require intrusive changes interfering with apps' functionalities. This paper introduces AndroLog a novel tool developed on top of the Soot framework, designed to provide fine-grained coverage information at multiple levels, including class, methods, statements, and Android components. In contrast to existing tools, AndroLog leaves the responsibility to test apps to analysts, and its motto is simplicity. As demonstrated in this paper, AndroLog can instrument up to 98% of recent Android apps compared to existing tools with 79% and 48% respectively for COSMO and ACVTool. AndroLog also stands out for its potential for future enhancements to increase granularity on demand. We make AndroLog available to the community and provide a video demonstration of AndroLog (see section 8).