mirror of
https://github.com/google-gemini/gemini-cli.git
synced 2026-07-07 08:37:44 -07:00
feat: consolidate backlog analysis tools into scripts/backlog-analysis
This commit is contained in:
Coco Sheng
@@ -0,0 +1,178 @@
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import json
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import urllib.request
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import urllib.error
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import os
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import concurrent.futures
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import subprocess
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import sys
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API_KEY = "REDACTED_API_KEY"
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MODEL = "gemini-3-flash-preview"
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URL = f"https://generativelanguage.googleapis.com/v1beta/models/{MODEL}:generateContent?key={API_KEY}"
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BUGS_FILE = 'data/bugs.json'
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with open(BUGS_FILE, 'r') as f:
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bugs = json.load(f)
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tools = [
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{
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"functionDeclarations": [
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{
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"name": "search_codebase",
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"description": "Search the gemini-cli packages directory for a string using grep. Returns matching lines and file paths.",
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"parameters": {
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"type": "OBJECT",
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"properties": {
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"pattern": {"type": "STRING", "description": "The text pattern to search for"}
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},
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"required": ["pattern"]
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}
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},
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{
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"name": "read_file",
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"description": "Read a specific file to understand its context.",
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"parameters": {
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"type": "OBJECT",
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"properties": {
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"filepath": {"type": "STRING", "description": "The path to the file"}
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},
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"required": ["filepath"]
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}
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}
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]
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}
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]
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def call_gemini(messages):
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data = {
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"contents": messages,
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"tools": tools,
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"generationConfig": {"temperature": 0.1}
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}
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req = urllib.request.Request(URL, data=json.dumps(data).encode('utf-8'), headers={'Content-Type': 'application/json'})
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with urllib.request.urlopen(req) as response:
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return json.loads(response.read().decode('utf-8'))
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def execute_tool(call):
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name = call['name']
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args = call.get('args', {})
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print(f" [TOOL CALL] {name}({args})", flush=True)
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if name == 'search_codebase':
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pattern = args.get('pattern', '')
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pattern = pattern.replace('"', '\\"')
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try:
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cmd = f'grep -rn "{pattern}" ../../packages | grep -vE "node_modules|dist|build" | head -n 30'
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res = subprocess.check_output(cmd, shell=True, text=True, stderr=subprocess.STDOUT)
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return res if res else "No matches found."
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except subprocess.CalledProcessError as e:
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return e.output if e.output else "No matches found."
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elif name == 'read_file':
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filepath = args.get('filepath', '')
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if not filepath.startswith('/'):
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filepath = os.path.join('../../packages', filepath)
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try:
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if not os.path.exists(filepath):
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return f"File {filepath} not found."
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cmd = f'head -n 200 "{filepath}"'
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res = subprocess.check_output(cmd, shell=True, text=True, stderr=subprocess.STDOUT)
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return res
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except Exception as e:
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return str(e)
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return "Unknown tool"
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def analyze_issue(issue):
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system_instruction = """You are a senior software engineer analyzing bug reports for the gemini-cli codebase.
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You MUST use the provided tools to investigate the codebase and pinpoint exactly which files and logic are responsible for the bug.
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DO NOT GUESS. You should explore the packages directory to find the relevant code.
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Rating Effort Level:
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- small (1 day): Bug is easy to reproduce, the cause is clear, and the fix is localized to 1-2 files.
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- medium (2-3 days): Bug is hard to reproduce (specific platform/setup), requires significant investigation, or touches multiple components.
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- large (>3 days): Requires architectural changes, deep refactoring, or affects core protocols.
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CRITICAL REPRODUCTION RULE:
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If a bug is hard to reproduce (e.g. needs specific OS like Windows/WSL2, complex external service setup, or is described as intermittent/rare), it MUST NOT be rated as small.
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Output format (ONLY valid JSON, no markdown):
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{
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"analysis": "technical analysis of root cause and fix",
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"effort_level": "small|medium|large",
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"reasoning": "justification with specific files/logic you found using the tools",
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"recommended_implementation": "code snippets or specific logic changes (only if small)"
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}
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"""
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prompt = f"{system_instruction}\n\nBug Title: {issue.get('title')}\nBug Body: {issue.get('body', '')[:1000]}"
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messages = [{"role": "user", "parts": [{"text": prompt}]}]
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for turn in range(8):
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try:
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res = call_gemini(messages)
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candidate = res['candidates'][0]['content']
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parts = candidate.get('parts', [])
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if 'role' not in candidate:
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candidate['role'] = 'model'
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messages.append(candidate)
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function_calls = [p for p in parts if 'functionCall' in p]
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if function_calls:
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tool_responses = []
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for fcall in function_calls:
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call_data = fcall['functionCall']
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result = execute_tool(call_data)
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tool_responses.append({
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"functionResponse": {
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"name": call_data['name'],
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"response": {"result": result[:5000]}
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}
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})
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messages.append({"role": "user", "parts": tool_responses})
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else:
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text = parts[0].get('text', '')
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if not text:
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continue
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if '```json' in text:
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text = text.split('```json')[1].split('```')[0]
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elif '```' in text:
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text = text.split('```')[1].split('```')[0]
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return json.loads(text.strip())
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except Exception as e:
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# print(f"Error on turn {turn}: {e}")
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break
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return {"analysis": "Failed to analyze autonomously", "effort_level": "medium", "reasoning": "Agent loop exceeded turn limit or errored."}
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def process_issue(issue):
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# Only skip if we have a real analysis and it's not "Failed..."
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if 'analysis' in issue and issue['analysis'] and issue['analysis'] != "Failed to analyze autonomously":
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return issue
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print(f"Analyzing Bug #{issue['number']}...", flush=True)
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result = analyze_issue(issue)
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issue['analysis'] = result.get('analysis', '')
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issue['effort_level'] = result.get('effort_level', 'medium')
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issue['reasoning'] = result.get('reasoning', '')
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if 'recommended_implementation' in result:
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issue['recommended_implementation'] = result['recommended_implementation']
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print(f"Completed Bug #{issue['number']} -> {issue['effort_level']}", flush=True)
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return issue
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def main():
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print(f"Starting agentic analysis for {len(bugs)} bugs...", flush=True)
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# Using small concurrency to avoid rate limits and keep logs readable
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with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
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futures = {executor.submit(process_issue, issue): issue for issue in bugs[5:]}
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for future in concurrent.futures.as_completed(futures):
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with open(BUGS_FILE, 'w') as f:
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json.dump(bugs, f, indent=2)
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print("Agentic analysis complete. `bugs.json` is updated.", flush=True)
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if __name__ == '__main__':
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main()
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