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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 Exploratory Study of Writing and Revising Explicit Pro...
Maryam Arab, Thomas D LaToza, Amy J Ko · 2020-04-02 · via cs.SE updates on arXiv.org

Knowledge sharing plays a crucial role throughout all software application development activities. When programmers learn and share through media like Stack overflow, GitHub, Meetups, videos, discussion forums, wikis, and blogs, every developer benefits. However, there is one kind of knowledge that developers share far less often: strategic knowledge for how to approach programming problems (e.g., how to debug server-side Python errors, how to resolve a merge conflict, how to evaluate the stability of an API one is considering for adoption). In this paper, we investigate the feasibility of developers articulating and sharing their strategic knowledge, and the use of these strategies to support other developers in their problem-solving. We specifically investigate challenges that developers face in articulating strategies in a form in which other developers can use to increase their productivity. To observe this, we simulated a knowledge-sharing platform, asking experts to articulate one of their own strategies and then asked the second set of developers to try to use the strategies and provide feedback on the strategies to authors. During the study, we asked both strategy authors and users to reflect on the challenges they faced. In analyzing the strategies authors created, the use of the strategies, the feedback that users provided to authors, and the difficulties that authors faced addressing this feedback, we found that developers can share strategic knowledge, but authoring strategies require substantial feedback from diverse audiences to be helpful to programmers with varying prior knowledge. Our results also raise challenging questions about how future work should support searching and browsing for strategies that support varying prior knowledge.