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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
Generalized Software Product Line Extraction
Federico Bruzzone, Walter Cazzola, Luca Favalli · 2026-05-28 · via cs.SE updates on arXiv.org

Software product line (SPL) engineering has been successfully applied to software development by obtaining software systems as compositions of modular features. Existing approaches to SPL engineering, however, are typically bound to a specific technological space (such as, a programming language and a composer) and integrated development environment (IDE), and rely on extraction mechanisms that make strong assumptions on the underlying technological space. This tight coupling hinders reuse, evolution, and adoption of heterogeneous development environments. We propose a general, workbench-agnostic protocol for extracting feature models from existing software artifacts and for configuring and deriving software products. The protocol follows a bottom-up approach based on lightweight dependency units called "atoms", and organizes the extraction and configuration process around an SPL server (workbench-independent) and an SPL client with a workbench-specific backend and a generic frontend. The protocol makes few assumptions on the underlying software artifacts and is therefore applicable to varied SPLs. The applicability of this approach is presented through a prototypical implementation of the architecture in which several subsystems interact and can be swapped freely without affecting the others. In particular, we focus on the application of such a protocol in the context of language product lines (LPLs), demonstrating its applicability to concrete scenarios while preserving workbench-agnosticism. From bottom to top, the implementation comprises: Neverlang language artifacts, a Java SPL client backend, an agnostic and reusable SPL server written in Go and Prolog, and a JavaScript SPL client frontend.