# Debugging AI-Generated Code (`aspiring_hypotenuse_ior/ai-generated-code-debugging-overhead`) Actor

45% of developers report debugging AI-generated code is more time-consuming than debugging their own; 66% cite 'AI solutions that are almost right, but not quite' as their single biggest frustration (2025 SO Developer Survey).

- **URL**: https://apify.com/aspiring\_hypotenuse\_ior/ai-generated-code-debugging-overhead.md
- **Developed by:** [Lore Nest](https://apify.com/aspiring_hypotenuse_ior) (community)
- **Categories:** Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $6.16 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Debugging AI-Generated Code

45% of developers report debugging AI-generated code is more time-consuming than debugging their own; 66% cite 'AI solutions that are almost right, but not quite' as their single biggest frustration (2025 SO Developer Survey).

AI-generated code often looks plausible at first glance but harbors subtle flaws — hallucinated methods, missing edge cases, and unchecked error paths — that turn debugging into a time sink. This tool surfaces those "almost right" problems before they reach production by statically scanning source code for the specific anti-patterns that AI assistants frequently introduce.

### Use Cases

- **Pre-review lint pass for AI-assisted commits**: Run the validator on a diff before opening a pull request to catch missing `try/except` blocks and absent `None` checks that generative models tend to omit.
- **Auditing LLM tutoring sessions**: When learners submit code copied from an AI tutor, instructors can identify whether the snippet includes hallucinated method calls (e.g., methods matching patterns like `.get_data_from_source()` or `.process_input_stream()`) that would never resolve against a real library.
- **Flagging fragile refactors**: Spot code sections lacking defensive programming so reviewers know which blocks to harden before merging into a shared codebase.
- **Complexity triage on auto-generated snippets**: Use the heuristic nesting- and density-based complexity score to decide whether a generated function is simple enough to trust at a glance or warrants deeper manual review.
- **Curriculum and training data QA**: Filter a dataset of model outputs to retain only snippets that pass baseline edge-case and error-handling checks, improving the quality of fine-tuning corpora.

### How It Works

The `CodeValidator` class splits the input source into individual lines and runs three independent heuristic checks. `_check_hallucinated_methods` applies a curated set of regular expressions to flag method invocations that match suspiciously generic, AI-typical naming patterns rather than real library APIs. `_check_missing_edge_cases` performs a textual scan to verify that the code contains `try`/`except` constructs and explicit `is None` guards, returning suggestions when neither is present. Finally, `_check_complexity` aggregates an indentation- and density-based score so callers can gauge how much cognitive overhead the snippet carries. Results from all checks are bundled into a `DiagnosticResult` dataclass that reports validity, the offending error category, a list of actionable suggestions, and the complexity score.

### Usage on Apify

Run it directly from the Apify Console ("Start"/"Try for free"), or call it via the API:

```
POST https://api.apify.com/v2/acts/ai-generated-code-debugging-overhead/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>
Content-Type: application/json

{}
```

### Pricing

Pay-per-event, billed automatically by Apify -- no separate account or payment step:

- Actor start: $0.00005 per GB of memory (minimum one event per run)
- Result: $0.005 per item returned -- this is the primary, usage-based charge

Also independently available at $0.0100 USDC (Base) per call via the x402 payment protocol (`POST /tools/ai-generated-code-debugging-overhead`) for callers outside the Apify platform.

### Example output

Real output captured from this tool's own build-time smoke test (input above):

```json
{
  "status": "error",
  "message": "No code provided for analysis"
}
```

# Actor input Schema

## `code` (type: `string`):

Real parameter read as payload\['code'] in this tool's own generated code.

## Actor input object example

```json
{
  "code": ""
}
```

# Actor output Schema

## `output` (type: `string`):

Real dataset items pushed via Actor.push\_data() in src/main.py -- each item is exactly the dict this tool's own run() returns. This tool's own real, build-time-captured output had these top-level fields: message, status.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("aspiring_hypotenuse_ior/ai-generated-code-debugging-overhead").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("aspiring_hypotenuse_ior/ai-generated-code-debugging-overhead").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call aspiring_hypotenuse_ior/ai-generated-code-debugging-overhead --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,aspiring_hypotenuse_ior/ai-generated-code-debugging-overhead"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/2r8lEauMHGRCGdtFU/builds/AhafgEuXdkNhzE361/openapi.json