惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 三生石上(FineUI控件)
博客园 - 叶小钗
博客园 - 聂微东
博客园 - 司徒正美
Hugging Face - Blog
Hugging Face - Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Google DeepMind News
Google DeepMind News
Recent Announcements
Recent Announcements
IT之家
IT之家
J
Java Code Geeks
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
I
InfoQ
爱范儿
爱范儿
Vercel News
Vercel News
Apple Machine Learning Research
Apple Machine Learning Research
阮一峰的网络日志
阮一峰的网络日志
博客园 - Franky
U
Unit 42
酷 壳 – CoolShell
酷 壳 – CoolShell
腾讯CDC
F
Fortinet All Blogs
V
Visual Studio Blog
人人都是产品经理
人人都是产品经理

cs updates on arXiv.org

Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment Agentic Proving for Program Verification MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts Solving the Aircraft Disassembly Scheduling Problem Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents CP or DP? Why Not Both: A Case Study in the Partial Shop Scheduling Problem Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction Naturalistic measure of social norms alignment Articulatory strategy as a source of variation in acoustic vowel dynamics When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning Cultural Adaptation in Large Language Models for Political Discourse Emotion Recognition in Sign Language Conversation ClimateChat-300K: A Multi-Modal Facebook Dataset for Understanding Diverse Perspectives in Climate Communication AraHopeCorpus: Annotation Guidelines and Dataset for Hope Speech in Arabic Social Media Crisis Discourse Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning DART: Semantic Recoverability for Structured Tool Agents Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems Parallel Context Compaction for Long-Horizon LLM Agent Serving
Generalized Software Product Line Extraction
Federico Bruzzone, Walter Cazzola, Luca Favalli · 2026-05-28 · via cs 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.