# W-series — current v9 knowledge-card index

Recorded corpus knowledge only. These cards are not copies of every full original paper or article. Publication links identify external sources; access and verification limitations remain unchanged.

- [W001 — AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development](Source_Cards/W001.md)
- [W002 — Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows](Source_Cards/W002.md)
- [W003 — The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers](Source_Cards/W003.md)
- [W004 — Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects](Source_Cards/W004.md)
- [W005 — Early Adoption of Agentic Coding Tools by GitHub Projects](Source_Cards/W005.md)
- [W006 — Agentic Much? Adoption of Coding Agents on GitHub](Source_Cards/W006.md)
- [W007 — Usage, Effects and Requirements for AI Coding Assistants in the Enterprise: An Empirical Study](Source_Cards/W007.md)
- [W008 — How Much Does AI Impact Development Speed? An Enterprise-Based Randomized Controlled Trial](Source_Cards/W008.md)
- [W009 — Productivity Assessment of Neural Code Completion](Source_Cards/W009.md)
- [W010 — Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools](Source_Cards/W010.md)
- [W011 — The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Development](Source_Cards/W011.md)
- [W012 — Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale](Source_Cards/W012.md)
- [W013 — Quantifying the Expectation-Realisation Gap for Agentic AI Systems](Source_Cards/W013.md)
- [W014 — Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI](Source_Cards/W014.md)
- [W015 — Agentic Delegation and the Language Frontier of Software Developers: A Model and Evidence from Claude Code on GitHub](Source_Cards/W015.md)
- [W016 — The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study](Source_Cards/W016.md)
- [W017 — Randomized Controlled Trials for Conditional Access Optimization Agent](Source_Cards/W017.md)
- [W018 — Intuition to Evidence: Measuring AI's True Impact on Developer Productivity](Source_Cards/W018.md)
- [W019 — Generative AI and labour productivity: a field experiment on coding](Source_Cards/W019.md)
- [W020 — Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity](Source_Cards/W020.md)
- [W021 — The AI Engineering Report 2026: The Acceleration Whiplash](Source_Cards/W021.md)
- [W022 — AI Coding Agents: Adoption Trends](Source_Cards/W022.md)
- [W023 — Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](Source_Cards/W023.md)
- [W024 — 2025 State of AI-Assisted Software Development](Source_Cards/W024.md)
- [W025 — Estimating AI productivity gains from Claude conversations](Source_Cards/W025.md)
- [W026 — Accelerate State of DevOps Report 2024](Source_Cards/W026.md)
- [W027 — Beyond the Copilot: Scaling the Agentic Product Development Life Cycle](Source_Cards/W027.md)
- [W028 — The AI Revolution in Software Development](Source_Cards/W028.md)
- [W029 — Beyond Bug Fixes: An Empirical Investigation of Post-Merge Code Quality Issues in Agent-Generated Pull Requests](Source_Cards/W029.md)
- [W030 — Workspace Topology as an Attack Vector in Agentic Coding Assistants](Source_Cards/W030.md)
- [W031 — More Code, Less Reuse: Investigating Code Quality and Reviewer Sentiment towards AI-generated Pull Requests](Source_Cards/W031.md)
- [W032 — Understanding the Impact of AI Code Assistants on Security API Usage](Source_Cards/W032.md)
- [W033 — How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests](Source_Cards/W033.md)
- [W034 — Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance](Source_Cards/W034.md)
- [W035 — Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub](Source_Cards/W035.md)
- [W036 — On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](Source_Cards/W036.md)
- [W037 — After the Party: Growth, Governance, and Security Scanning in the OpenClaw Agent Skill Ecosystem](Source_Cards/W037.md)
- [W038 — Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation](Source_Cards/W038.md)
- [W039 — Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity](Source_Cards/W039.md)
- [W040 — Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study](Source_Cards/W040.md)
- [W041 — Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories](Source_Cards/W041.md)
- [W042 — Security Degradation in Iterative AI Code Generation: A Systematic Analysis of the Paradox](Source_Cards/W042.md)
- [W043 — We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs](Source_Cards/W043.md)
- [W044 — An Empirical Study of Code Clones from Commercial AI Code Generators](Source_Cards/W044.md)
- [W045 — Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks](Source_Cards/W045.md)
- [W046 — Do Users Write More Insecure Code with AI Assistants?](Source_Cards/W046.md)
- [W047 — Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants](Source_Cards/W047.md)
- [W048 — Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](Source_Cards/W048.md)
- [W049 — The Range Shrinks, the Threat Remains: Re-evaluating LLM Package Hallucinations on the 2026 Frontier-Model Cohort](Source_Cards/W049.md)
- [W050 — Understanding the (In)Security of Vibe-Coded Applications](Source_Cards/W050.md)
- [W051 — From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software](Source_Cards/W051.md)
- [W052 — What is the Difference Between Me and You? Benchmarking the Quality Gap Between Human-Written and AI-Generated Code](Source_Cards/W052.md)
- [W053 — Scanning the Harness: Configuration Exposures in AI Coding-Agent Supply Chains](Source_Cards/W053.md)
- [W054 — Not All Agents Are Equal: Code Quality and Post-Merge Maintenance Across Five Autonomous Coding Agents in the Wild](Source_Cards/W054.md)
- [W055 — Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild](Source_Cards/W055.md)
- [W056 — A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories](Source_Cards/W056.md)
- [W057 — How Do AI Coding Agents Contribute to Software Development? An Empirical Study of Agentic Pull Requests](Source_Cards/W057.md)
- [W058 — Decoupling Code Complexity from Newcomer Participation: A Causal Study of AI Coding Agent Adoption in OSS](Source_Cards/W058.md)
- [W059 — AI-Generated Smells: An Analysis of Code and Architecture in LLM- and Agent-Driven Development](Source_Cards/W059.md)
- [W060 — Your AI, My Shell: Demystifying Prompt Injection Attacks on Agentic AI Coding Editors](Source_Cards/W060.md)
- [W061 — 2025 GenAI Code Security Report](Source_Cards/W061.md)
- [W062 — Vibe Security Radar](Source_Cards/W062.md)
- [W063 — An Endless Stream of AI Slop: How Developers Discuss the Burden of AI-Assisted Software Development](Source_Cards/W063.md)
- [W064 — Studying Quality Improvements Recommended via Manual and Automated Code Review](Source_Cards/W064.md)
- [W065 — These Aren't the Reviews You're Looking For: How Humans Review AI-Generated Pull Requests](Source_Cards/W065.md)
- [W066 — Evaluating the Impact of Explainable AI on Trust in AI-Assisted Code Review](Source_Cards/W066.md)
- [W067 — (Don't) Trust, but (Don't) Verify: Developers' Attention to Security in AI-Generated Code](Source_Cards/W067.md)
- [W068 — Behind Agentic Pull Requests: An Empirical Study on Developer Interventions in AI Agent-Authored Pull Requests](Source_Cards/W068.md)
- [W069 — Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code](Source_Cards/W069.md)
- [W070 — Impact of an LLM-based Review Assistant in Practice: A Mixed Open-/Closed-source Case Study](Source_Cards/W070.md)
- [W071 — Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study](Source_Cards/W071.md)
- [W072 — AI-to-AI Code Reviews of GitHub Pull Requests](Source_Cards/W072.md)
- [W073 — The Effect of Complexity and Provenance on Code Review Decisions: Evidence from a Controlled Experiment](Source_Cards/W073.md)
- [W074 — RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian](Source_Cards/W074.md)
- [W075 — Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in LLMs](Source_Cards/W075.md)
- [W076 — SWR-Bench: Assessing LLM Performance in Real-World Code Review Comment Generation](Source_Cards/W076.md)
- [W077 — Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests](Source_Cards/W077.md)
- [W078 — Beyond the Surface: Measuring Self-Preference in LLM Judgments](Source_Cards/W078.md)
- [W079 — Correlated Errors in Large Language Models](Source_Cards/W079.md)
- [W080 — Unit Testing Past vs. Present: Examining LLMs' Impact on Defect Detection and Efficiency](Source_Cards/W080.md)
- [W081 — Deep Learning-based Code Reviews: A Paradigm Shift or a Double-Edged Sword?](Source_Cards/W081.md)
- [W082 — LLM Evaluators Recognize and Favor Their Own Generations](Source_Cards/W082.md)
- [W083 — A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions](Source_Cards/W083.md)
- [W084 — Self-Preference Bias in LLM-as-a-Judge](Source_Cards/W084.md)
- [W085 — Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance](Source_Cards/W085.md)
- [W086 — Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension](Source_Cards/W086.md)
- [W087 — Do as AI say: susceptibility in deployment of clinical decision-aids](Source_Cards/W087.md)
- [W088 — Trustworthiness Perceptions in Code Review: An Eye-tracking Study](Source_Cards/W088.md)
- [W089 — The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems](Source_Cards/W089.md)
- [W090 — Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise](Source_Cards/W090.md)
- [W091 — Automation bias and verification complexity: a systematic review](Source_Cards/W091.md)
- [W092 — Complacency and Bias in Human Use of Automation: An Attentional Integration](Source_Cards/W092.md)
- [W093 — Automating Low-Risk Code Review at Meta: RADAR, Risk Calibration, and Review Efficiency](Source_Cards/W093.md)
- [W094 — AI Slop is DDoSing Open Source: Understanding the Impact of AI-Generated Contributions on Open Source Sustainability](Source_Cards/W094.md)
- [W095 — Beyond Code Generation: Reliability, Verification, and Cost Economics in the Agentic Software Development Lifecycle](Source_Cards/W095.md)
- [W096 — Go Home Copilot, You're Drunk: Understanding Developer Responses to Agent-Generated Code Review Comments](Source_Cards/W096.md)
- [W097 — Programmers Are Poor and Overconfident Judges of LLM-Generated Assertions](Source_Cards/W097.md)
- [W098 — Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wild](Source_Cards/W098.md)
- [W099 — From Code Review to Code Critique: Intent, Drift, and Spotlight for AI-Generated Diffs at Scale](Source_Cards/W099.md)
- [W100 — The End of Code Review: Coding Agents Supersede Human Inspection](Source_Cards/W100.md)
- [W101 — Articulate but Wrong: Self-Review Failures in LLM-Based Code Modernization](Source_Cards/W101.md)
- [W102 — Cross-Model LLM Code Review: Should You Use Claude to Review Codex or Vice Versa?](Source_Cards/W102.md)
- [W103 — An Eye for Trust: An Exploration of Developers' Trust Perceptions Through Urgency and Reputation](Source_Cards/W103.md)
- [W104 — Coding with Enemy: Can Human Developers Detect AI Agent Sabotage?](Source_Cards/W104.md)
- [W105 — Beyond Lexical Metrics: Sentence-Embedding Detection of Reviewer Habituation in AI Code Review](Source_Cards/W105.md)
- [W106 — Code Review Agent Benchmark (c-CRAB)](Source_Cards/W106.md)
- [W107 — Human-AI Synergy in Agentic Code Review](Source_Cards/W107.md)
- [W108 — Do LLM Evaluators Prefer Themselves for a Reason?](Source_Cards/W108.md)
- [W109 — LLM Critics Help Catch LLM Bugs](Source_Cards/W109.md)
- [W110 — Overreliance on AI: Literature Review](Source_Cards/W110.md)
- [W111 — Many SWE-bench-Passing PRs Would Not Be Merged into Main](Source_Cards/W111.md)
- [W112 — Novice Developers Produce Larger Review Overhead for Project Maintainers while Vibe Coding](Source_Cards/W112.md)
- [W113 — I Would Have Written My Code Differently: Beginners Struggle to Understand LLM-Generated Code](Source_Cards/W113.md)
- [W114 — Correlates of programmer efficacy and their link to experience: a combined EEG and eye-tracking study](Source_Cards/W114.md)
- [W115 — Novice Software Developers, All Over Again](Source_Cards/W115.md)
- [W116 — Using Biometrics to Understand AI-Assisted Coding Performance and its Perception](Source_Cards/W116.md)
- [W117 — The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading](Source_Cards/W117.md)
- [W118 — How AI Impacts Skill Formation](Source_Cards/W118.md)
- [W119 — Training in the Age of AI: A Theory of Career Viability](Source_Cards/W119.md)
- [W120 — Automation, AI, and the Intergenerational Transmission of Knowledge](Source_Cards/W120.md)
- [W121 — Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](Source_Cards/W121.md)
- [W122 — AI Didn't Make Programming Easier. It Just Made It Differently Difficult](Source_Cards/W122.md)
- [W123 — Firms' GitHub Copilot Adoption and Labor Market Outcomes for Software Engineers](Source_Cards/W123.md)
- [W124 — Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives](Source_Cards/W124.md)
- [W125 — Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence](Source_Cards/W125.md)
- [W126 — How Does AI Change Labor Demand? Evidence from 41 Countries](Source_Cards/W126.md)
- [W127 — Remote Work Leaves Younger Workers Sidelined](Source_Cards/W127.md)
- [W128 — Labor Market Consequences of Generative AI: Early Evidence from Norway](Source_Cards/W128.md)
- [W129 — AI Has Not Impacted the Youth Labor Market in Finland](Source_Cards/W129.md)
- [W130 — A New Look at AI's Impact on Jobs: Firm-Level AI Spending and Workforce Adjustment](Source_Cards/W130.md)
- [W131 — The Broken Ladder: AI, Remote Work, and Early-Career Hiring](Source_Cards/W131.md)
- [W132 — New Jobs in 140 Years of Data: Why the AI Displacement Fear Is Overstated — and What to Worry About Instead](Source_Cards/W132.md)
- [W133 — Firm Data on AI](Source_Cards/W133.md)
- [W134 — Artificial Intelligence and the Labor Market](Source_Cards/W134.md)
- [W135 — Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data](Source_Cards/W135.md)
- [W136 — Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI](Source_Cards/W136.md)
- [W137 — The Labor Market Is Tilting Toward Seniority](Source_Cards/W137.md)
- [W138 — AI's Impact on Labor and Hiring](Source_Cards/W138.md)
- [W139 — Do Job Postings Show Early Labor-Market Effects of AI?](Source_Cards/W139.md)
- [W140 — AI and Job Postings: From Destruction to Creation?](Source_Cards/W140.md)
- [W141 — Google's AI & Economy ATLAS v1.0: Mapping Gemini Usage in the Economy](Source_Cards/W141.md)
- [W142 — AI Is Probably Not (Yet) the Reason for Labor Market Weakening](Source_Cards/W142.md)
- [W143 — New data show no AI jobs apocalypse — for now](Source_Cards/W143.md)
- [W144 — 2026 Global AI Jobs Barometer: Two futures for jobs in an AI era](Source_Cards/W144.md)
- [W145 — ROI of AI-Assisted Software Development](Source_Cards/W145.md)
- [W146 — McKinsey Technology Trends Outlook 2026](Source_Cards/W146.md)
- [W147 — To Ban or not to Ban? How Open Source Projects Govern GenAI Contributions](Source_Cards/W147.md)
- [W148 — SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents](Source_Cards/W148.md)
- [W149 — Making AI Visible, Not Vanished: How AI Policies Reshape Developer Experience on GitHub](Source_Cards/W149.md)
- [W150 — We Permit the Use of AI, but [...]: The Landscape of AI Policies in Popular Open Source Projects](Source_Cards/W150.md)
- [W151 — Blueprint First, Model Second: A Framework for Deterministic LLM Workflow](Source_Cards/W151.md)
- [W152 — Agentic Software Engineering: Foundational Pillars and a Research Roadmap](Source_Cards/W152.md)
- [W153 — Toward Agentic Software Engineering Beyond Code: Framing Vision, Values, and Vocabulary](Source_Cards/W153.md)
- [W154 — Comprehension Debt in GenAI-Assisted Software Engineering Projects](Source_Cards/W154.md)
- [W155 — ChatGPT: Friend or Foe When Comprehending and Changing Unfamiliar Code](Source_Cards/W155.md)
- [W156 — LACY: Simulating Expert Mentoring for Software Onboarding with Code Tours](Source_Cards/W156.md)
- [W157 — ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents](Source_Cards/W157.md)
- [W158 — Should I Give Up Now? Investigating LLM Pitfalls in Software Engineering](Source_Cards/W158.md)
- [W159 — Memory Management and Contextual Consistency for Long-Running Low-Code Agents](Source_Cards/W159.md)
- [W160 — AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents](Source_Cards/W160.md)
- [W161 — Examining ownership models in software teams: A systematic literature review and a replication study](Source_Cards/W161.md)
- [W162 — 40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study](Source_Cards/W162.md)
- [W163 — A Case Study of Onboarding in Software Teams: Tasks and Strategies](Source_Cards/W163.md)
- [W164 — Program Comprehension and Code Complexity Metrics: An fMRI Study](Source_Cards/W164.md)
- [W165 — Please Turn Your Cameras On: Remote Onboarding of Software Developers during a Pandemic](Source_Cards/W165.md)
- [W166 — Comprehension of computer code relies primarily on domain-general executive brain regions](Source_Cards/W166.md)
- [W167 — A Look into Programmers' Heads](Source_Cards/W167.md)
- [W168 — Do you remember this source code?](Source_Cards/W168.md)
- [W169 — Simultaneous Measurement of Program Comprehension with fMRI and Eye Tracking: A Case Study](Source_Cards/W169.md)
- [W170 — Measuring Program Comprehension: A Large-Scale Field Study with Professionals](Source_Cards/W170.md)
- [W171 — Revisiting Turnover-Induced Knowledge Loss in Software Projects](Source_Cards/W171.md)
- [W172 — Measuring neural efficiency of program comprehension](Source_Cards/W172.md)
- [W173 — Quantifying and Mitigating Turnover-Induced Knowledge Loss: Case Studies of Chrome and a Project at Avaya](Source_Cards/W173.md)
- [W174 — Impact of Developer Turnover on Quality in Open-Source Software](Source_Cards/W174.md)
- [W175 — Code Ownership and Software Quality: A Replication Study](Source_Cards/W175.md)
- [W176 — I Know What You Did Last Summer — An Investigation of How Developers Spend Their Time](Source_Cards/W176.md)
- [W177 — Stuck and Frustrated or in Flow and Happy: Sensing Developers' Emotions and Progress](Source_Cards/W177.md)
- [W178 — Ramp-Up Journey of New Hires: Tug of War of Aids and Impediments](Source_Cards/W178.md)
- [W179 — A systematic literature review on the usage of eye-tracking in software engineering](Source_Cards/W179.md)
- [W180 — Using psycho-physiological measures to assess task difficulty in software development](Source_Cards/W180.md)
- [W181 — Understanding understanding source code with functional magnetic resonance imaging](Source_Cards/W181.md)
- [W182 — Don't Touch My Code! Examining the Effects of Ownership on Software Quality](Source_Cards/W182.md)
- [W183 — Moving into a New Software Project Landscape](Source_Cards/W183.md)
- [W184 — Developer Fluency: Achieving True Mastery in Software Projects](Source_Cards/W184.md)
- [W185 — Succession: Measuring Transfer of Code and Developer Productivity](Source_Cards/W185.md)
- [W186 — Maintaining Mental Models: A Study of Developer Work Habits](Source_Cards/W186.md)
- [W187 — Globalization by Chunking: A Quantitative Approach](Source_Cards/W187.md)
- [W188 — The Ramp-Up Problem in Software Projects: A Case Study of How Software Immigrants Naturalize](Source_Cards/W188.md)
- [W189 — The Substrate Collapse: AI Code Generation Invalidates Authorship-Based Knowledge Metrics](Source_Cards/W189.md)
- [W190 — How do Humans and LLMs Process Confusing Code?](Source_Cards/W190.md)
- [W191 — Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub](Source_Cards/W191.md)
- [W192 — AIDev: Studying AI Coding Agents on GitHub](Source_Cards/W192.md)
- [W193 — MirrorCode: Evidence that AI can already do some weeks-long coding tasks](Source_Cards/W193.md)