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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
An Investigation into Protestware
Tanner Finken, Jesse Chen, Sazzadur Rahaman · 2024-09-30 · via cs.SE updates on arXiv.org

Protests are public expressions of personal or collective discontent with the current state of affairs. Although traditional protests involve in-person events, the ubiquity of computers and software opened up a new avenue for activism: protestware. The roots of protestware date back to the early days of computing. However, recent events in the Russo-Ukrainian war has sparked a new wave of protestware. While news and media are heavily reporting on individual protestware as they are discovered, the understanding of such software as a whole is severely limited. In particular, we do not have a detailed understanding of their characteristics and their impact on the community. To address this gap, we first collect 32 samples of protestware. Then, with these samples, we formulate characteristics of protestware using inductive analysis. In addition, we analyze the aftermath of the protestware which has potential to affect the software supply chain in terms of community sentiment and usage. We report that: (1) protestware has three notable characteristics, namely, i) the "nature of inducing protest" is diverse, ii) the "nature of targeting users" is discriminatory, and iii) the "nature of transparency" is not always respected; (2) disruptive protestware may cause substantial adverse impact on downstream users; (3) developers of protestware may not shift their beliefs even with pushback; (4) the usage of protestware from JavaScript libraries has been seen to generally increase over time.