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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
Exception-aware Lifecycle Model Construction for Framewor...
Jiwei Yan, Jinhao Huang, Hengqin Yang, Jun Yan · 2024-01-05 · via cs.SE updates on arXiv.org

The implementation of complex software systems usually depends on low-level frameworks or third-party libraries. During their evolution, the APIs adding and removing behaviors may cause unexpected compatibility problems. So, precisely analyzing and constructing the framework/ library's API lifecycle model is of great importance. Existing works have proposed the API existence-changing model for defect detection, while not considering the influence of semantic changes in APIs. In some cases, developers will not remove or deprecate APIs but modify their semantics by adding, removing, or modifying their exception-thrown code, which may bring potential defects to upper-level code. Therefore, besides the API existence model, it is also necessary for developers to be concerned with the exception-related code evolution in APIs, which requires the construction of exception-aware API lifecycle models for framework/library projects. To achieve automatic exception-aware API lifecycle model construction, this paper adopts a static analysis technique to extract exception summary information in the framework API code and adopts a multi-step matching strategy to obtain the changing process of exceptions. Then, it generates exception-aware API lifecycle models for the given framework/library project. With this approach, the API lifecycle extraction tool, JavaExP, is implemented, which is based on Java bytecode analysis. Compared to the state-of-the-art tool, JavaExP achieves both a higher F1 score (+60%) and efficiency (+7x), whose precision of exception matching and changing results is 98%. Compared to the exception-unaware API lifecycle modeling on 60 versions, JavaExp can identify 18% times more API changes. Among the 75,433 APIs under analysis, 20% of APIs have changed their exception-throwing behavior at least once after API introduction, which may bring many hidden compatibility issues.