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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
Public Sector Platforms going Open: Creating and Growing ...
Johan Linåker, Per Runeson · 2022-08-01 · via cs.SE updates on arXiv.org

Background: By creating ecosystems around platforms of Open Source Software (OSS) and Open Data (OD), and adopting open collaborative development practices, platform providers may exploit open innovation benefits. However, adopting such practices in a traditionally closed organization is a maturity process that we hypothesize cannot be undergone without friction. Objective: This study aims to investigate what challenges may occur for a newly-turned platform provider in the public sector, aiming to adopt open collaborative practices to create an ecosystem around the development of the underpinning platform. Method: An exploratory case-study is conducted at a Swedish public sector platform provider, which is creating an ecosystem around OSS and OD, related to the labor market. Data is collected through interviews, document studies, and prolonged engagement. Results: Findings highlight a fear among developers of being publicly questioned for their work, as they represent a government agency undergoing constant scrutiny. Issue trackers, roadmaps, and development processes are generally closed, while multiple channels are used for communication, causing internal and external confusion. Some developers are reluctant to communicate externally as they believe it interferes with their work. Lack of health metrics limits possibilities to follow ecosystem growth and for actors to make investment decisions. Further, an autonomous team structure is reported to complicate internal communication and enforcement of the common vision, as well as collaboration. A set of interventions for addressing the challenges are proposed, based on related work. Conclusions: We conclude that several cultural, organizational, and process-related challenges may reside, and by understanding these early on, platform providers can be preemptive in their work of building healthy ecosystems.