The latest articles on DEV Community by codeforfree team (@codeforfree_team_3e62c052). https://dev.to/codeforfree_team_3e62c052 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4194727%2F835782b5-5df3-4439-ab23-31dc5a994aee.png https://dev.to/codeforfree_team_3e62c052 en codeforfree team Mon, 19 Oct 2026 19:09:38 +0000 https://dev.to/codeforfree_team_3e62c052/how-to-evaluate-an-ai-coding-assistant-on-a-real-task-eli https://dev.to/codeforfree_team_3e62c052/how-to-evaluate-an-ai-coding-assistant-on-a-real-task-eli <p>A transparent protocol for evaluating an AI coding assistant on a real software task — with pre-registered acceptance criteria, controlled conditions, and reproducible evidence.<br> Byline: [Author] · [Publication date] · Experiment protocol; results pending<br> Status label: Worked example; no product run claimed.</p> <p>Evaluate an AI coding assistant on a bounded, real task by pre-registering acceptance criteria, controlling conditions, running the task, checking the result against criteria, reviewing the patch, and rerunning from a clean state. Publish the evidence and limitations. One task shows only what happened in that recorded setup — not universal reliability.</p> <p>Sections (with expert depth):</p> <p>Why "it works" is not evidence.<br> Expert depth: Distinguishes demonstration from evidence; explains why "it compiled" ≠ "it's correct."<br> Pre-registering acceptance criteria (and why this matters for AI evaluation).<br> Expert depth: Explains why pre-registration prevents post-hoc rationalization; the AI can't redefine success after seeing the output.<br> Controlling conditions (model, tool, permissions, environment, time).<br> Expert depth: Explains why uncontrolled comparisons are meaningless; lists what to hold constant and what to disclose.<br> The run: what to capture and what to redact.<br> Expert depth: Explains why redaction is a design decision, not an afterthought.<br> Checking the result: behavior, not just compilation.<br> Expert depth: Explains why "tests pass" ≠ "behavior is correct"; covers edge cases.<br> Patch review: scope, correctness, regressions.<br> Expert depth: Explains why diff size is a signal; covers what to look for.<br> Clean rerun: the reproducibility test.<br> Expert depth: Explains why rerun is the hardest part and the most important.<br> What one task can and cannot show.<br> Expert depth: Explicit limits; no universal claims.</p> <p>This method works independently of SKY. Check the current product page for what SKY actually supports<br> visit -<a href="https://codeforfree.in/" rel="noopener noreferrer">https://codeforfree.in/</a>.</p> ai coding testing reproducibility