Kamesh Akella kami619

SWE-bench Experiments

Quantify AgentReady settings against SWE-bench baseline using both SWE-agent and Claude Code.

Quick Start

# 1. Run agent on repository
agentready experiment run-agent sweagent \
 --repo-path /path/to/repo \

Integrating MCP (Model Context Protocol) servers with GitHub for test analysis involves several coordinated steps to enable AI-driven, automated workflows for CI/CD and test management. Here’s a structured overview of the process:

1. Set Up the GitHub MCP Server

Generate a GitHub Personal Access Token:
Create a token with the necessary repository and workflow permissions to allow the MCP server to interact with your GitHub data[4][3].

Launch the MCP Server:
The official GitHub MCP server can be started using Docker:

docker run -i --rm -e GITHUB_PERSONAL_ACCESS_TOKEN=<your-token> ghcr.io/github/github-mcp-server

	# Red Hat & Open Data Hub Baseline Study
	# Curated list focused on RH product upstreams and ODH ecosystem
	# Target: 50+ repositories across AI/ML, Container, Platform, and DevOps domains

	# =============================================================================
	# CATEGORY A: Open Data Hub (ODH) GitHub Organization - AI/ML Platform
	# =============================================================================
	# Expected: Silver-Gold (actively maintained, Python-heavy, ML focus)

	# Core ODH Platform Components

	import argparse
	import torch
	import torch.nn.functional as F
	from transformers import AutoModelForSequenceClassification, AutoTokenizer
	# Placeholder for speech-to-text library (e.g., SpeechRecognition)
	# import speech_recognition as sr
	import os
	import numpy as np
	import pandas as pd
	from torch.utils.data import Dataset, TensorDataset, DataLoader, RandomSampler