K:Benchmarks and construct validity
Benchmarks and construct validity
Editorial topic mappings connect related questions across sources and book chapters.
SE@PoliMi bibliography 30
- W013 — Quantifying the Expectation-Realisation Gap for Agentic AI Systems
Lobentanzer · 2026 - 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
Murphy-Hill · 2026 - W037 — After the Party: Growth, Governance, and Security Scanning in the OpenClaw Agent Skill Ecosystem
Xiong · 2026 - W038 — Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation
Yan · 2026 - W041 — Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories
Schreiber · 2025 - W043 — We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs
Spracklen · 2025 - W045 — Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks
Zhao · 2025 - W048 — Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions
Pearce · 2022 - W049 — The Range Shrinks, the Threat Remains: Re-evaluating LLM Package Hallucinations on the 2026 Frontier-Model Cohort
Churilov · 2026 - W052 — What is the Difference Between Me and You? Benchmarking the Quality Gap Between Human-Written and AI-Generated Code
Improta · 2026 - W056 — A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories
Mao · 2026 - W059 — AI-Generated Smells: An Analysis of Code and Architecture in LLM- and Agent-Driven Development
Zhu · 2026 - W060 — Your AI, My Shell: Demystifying Prompt Injection Attacks on Agentic AI Coding Editors
Liu · 2025 - W061 — 2025 GenAI Code Security Report
Veracode · 2025 - W075 — Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in LLMs
Tsui · 2026 - W076 — SWR-Bench: Assessing LLM Performance in Real-World Code Review Comment Generation
Zeng · 2026 - W077 — Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests
Zhao · 2026 - W079 — Correlated Errors in Large Language Models
Kim · 2025 - W082 — LLM Evaluators Recognize and Favor Their Own Generations
Panickssery · 2024 - W101 — Articulate but Wrong: Self-Review Failures in LLM-Based Code Modernization
Purnachandra Reddy · 2026 - W102 — Cross-Model LLM Code Review: Should You Use Claude to Review Codex or Vice Versa?
Xiang · 2026 - W106 — Code Review Agent Benchmark (c-CRAB)
Zhang · 2026 - W108 — Do LLM Evaluators Prefer Themselves for a Reason?
Chen · 2025 - W111 — Many SWE-bench-Passing PRs Would Not Be Merged into Main
Whitfill · 2026 - W142 — AI Is Probably Not (Yet) the Reason for Labor Market Weakening
Nunn · 2026 - W148 — SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents
Lin · 2026 - W151 — Blueprint First, Model Second: A Framework for Deterministic LLM Workflow
Qiu · 2026 - W157 — ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents
Rafique · 2026 - W159 — Memory Management and Contextual Consistency for Long-Running Low-Code Agents
Xu · 2026 - W160 — AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
Anokhin · 2025
Addy Osmani 1
- O020 — How Good Is AI at Coding React (Really)?
Addy Osmani · 2025-12-29
Martin Fowler’s website 1
- F029 — Experiences with local models for coding
Birgitta Böckeler · 2026-07-08
Software Engineering Practices 14
- PAT:001 — Plan-Then-Execute
- PAT:004 — Initializer-Maintainer Dual Agent Architecture
- PAT:011 — Reward Shaping for Tool Use
- PAT:013 — Agent Reinforcement Fine-Tuning
- PAT:021 — Tool Call Budget
- PAT:023 — The External Bash Loop
- PAT:024 — The Stop-Hook Loop
- PAT:025 — The Editor-Native Manual Loop
- PAT:026 — Run Until Done: Retry Against Reality
- PAT:046 — Small Verifiable Chunks
- PAT:065 — Model Switching for Regeneration
- PAT:073 — Multi-Mode Agent
- PAT:081 — One-Item-Per-Cycle
- PAT:083 — Budget-Gated Autonomy