# GitHub Release Impact & Issue Regression Intelligence (`obliging_persimmon_cki/github-release-impact-issue-regression-intelligence`) Actor

Compare GitHub issue reporting before and after software releases to surface regression signals, emerging themes, coverage, and confidence.

- **URL**: https://apify.com/obliging\_persimmon\_cki/github-release-impact-issue-regression-intelligence.md
- **Developed by:** [Dung Huynh](https://apify.com/obliging_persimmon_cki) (community)
- **Categories:** AI, Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

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

## 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

## GitHub Release Impact & Issue Regression Intelligence

Analyze public GitHub issue reporting before and after a software release. This Actor resolves a release boundary, excludes pull requests returned by GitHub's Issues API, samples both periods deterministically, and reports issue-volume changes, bug/regression signals, emerging themes, worsening or decreasing themes, representative evidence, coverage, and confidence.

This is an impact-intelligence report, not a raw GitHub Issues scraper. Regression signals describe temporal changes observed after a release; they do not prove that the release caused an individual issue.

### Input

The default input compares the latest eligible release of `vitejs/vite` over seven-day windows. `comparisonMode` can be `latest_eligible_release`, `release_tag`, `custom_date`, or `recent_vs_previous`. `release_tag` requires one repository and a tag; a supported GitHub release URL can supply the tag. `custom_date` requires an ISO `comparisonDate`. `maxIssuesPerPeriod` controls the deterministic analysis sample, while bounded pagination collects enough history to cover both windows.

An optional `githubToken` increases public GitHub API rate limits. It is never logged or included in output. No token, browser, proxy, LLM, or external search service is required for the Store default.

### Output

One Dataset item is produced per requested repository. Successful and partial items contain the resolved boundary, before/after period metrics, impact direction and level, theme deltas, feature-request counts, bounded issue evidence, coverage, warnings, request statistics, and a deterministic summary. A repository failure becomes a failed item so other repositories can finish.

### Output fields

| Field | Meaning |
| --- | --- |
| `status` | Repository result status. |
| `repository.fullName` | Canonical owner/repository name. |
| `boundary.release.tag` | Exact selected release tag, when applicable. |
| `boundary.timestamp` | Comparison boundary timestamp. |
| `impact.direction` | Observed regression-signal direction. |
| `impact.level` | Low, medium, high, or unknown impact level. |
| `impact.issueVolume.before` | Issues analyzed before the boundary. |
| `impact.issueVolume.after` | Issues analyzed after the boundary. |
| `impact.issueVolume.absoluteDelta` | Difference in analyzed issue counts. |
| `impact.bugSignalRateDelta` | Change in bug-related signal share. |
| `impact.regressionSignalRateDelta` | Change in regression-related signal share. |
| `coverage.before.coverageStatus` | BEFORE evidence coverage. |
| `coverage.after.coverageStatus` | AFTER evidence coverage. |
| `impact.confidence.level` | Evidence-quality confidence level. |
| `summary` | Deterministic non-causal summary. |
| `warnings` | Structured collection and coverage warnings. |
| `error.code` | Failure code for failed repository items. |
| `fetchedAt` | Report generation timestamp. |

Coverage is first-class: `full` means the requested period was reached with enough analyzed issues and no known truncation; `partial` means usable evidence exists with an incomplete window or safety limitation; `insufficient` means the sample is too small for directional analysis. Confidence is an evidence-quality score, not a probability of causation.

### API behavior and limitations

The Actor uses GitHub REST release and repository Issues endpoints with `state=all`, chronological creation sorting, bounded pages, and `per_page=100`. GitHub's Issues endpoint can return pull requests; every item with `pull_request` is excluded. Comments are not crawled by default. V1 does not analyze discussions, pull requests, commits, diffs, source code, private repositories, security vulnerabilities, alerts, or webhooks. Rate limits produce structured warnings or partial output where useful.

### Scheduling and pricing

Schedule periodic runs to compare rolling windows or monitor releases. The intended Pay Per Event unit is one successful repository release report; pricing remains provisional until measured runtime and API economics are documented.

### FAQ

**Does this prove a release caused a bug?** No. It identifies elevated or changed issue-reporting signals around a boundary.

**Why can a recent release fall back to rolling mode?** A release must be old enough to provide the requested after window. If no eligible release is available, the report states the fallback explicitly.

**Why is coverage partial?** The period may still be active, contain too few issues, hit bounded pagination, or encounter a GitHub rate limit.

# Actor input Schema

## `repositories` (type: `array`):

One to five public GitHub repositories as owner/repo, repository URLs, or supported release URLs.

## `comparisonMode` (type: `string`):

Choose a release boundary, custom date, or rolling recent-vs-previous comparison.

## `releaseTag` (type: `string`):

Required for release\_tag mode. A release URL in repositories can supply this value.

## `comparisonDate` (type: `string`):

ISO date or datetime boundary for custom\_date mode.

## `windowDays` (type: `integer`):

Length of each before and after period.

## `maxIssuesPerPeriod` (type: `integer`):

Deterministic sample cap for each comparison period.

## `includeEvidence` (type: `boolean`):

Include bounded GitHub issue references supporting changed themes.

## `githubToken` (type: `string`):

Optional token for higher public GitHub API rate limits. It is never written to output or logs.

## Actor input object example

```json
{
  "repositories": [
    "vitejs/vite"
  ],
  "comparisonMode": "latest_eligible_release",
  "releaseTag": "",
  "comparisonDate": "",
  "windowDays": 7,
  "maxIssuesPerPeriod": 50,
  "includeEvidence": true
}
```

# 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("obliging_persimmon_cki/github-release-impact-issue-regression-intelligence").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("obliging_persimmon_cki/github-release-impact-issue-regression-intelligence").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 obliging_persimmon_cki/github-release-impact-issue-regression-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,obliging_persimmon_cki/github-release-impact-issue-regression-intelligence"
        }
    }
}

```

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/qRuanBknJ31f0gi1M/builds/QV9K7RU9h0hRZTOeT/openapi.json