apache-otava

v0.8.0 suspicious
4.0
Medium Risk

Apache Otava (Incubating): Change Detection for Continuous Performance Engineering

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some legitimate use cases but raises concerns due to its network and shell risks, which need further investigation.

  • network calls to localhost
  • use of subprocess.run
Per-check LLM notes
  • Network: The network calls appear to be connecting to localhost, which is less suspicious than external IPs but still requires context to determine legitimacy.
  • Shell: The use of subprocess.run to execute commands could indicate legitimate functionality if documented, but it also poses a risk for potential misuse or execution of unintended commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The author's details are incomplete and the license URL is non-secure, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 18 test file(s) found

  • Test runner config found: pyproject.toml
  • 18 test file(s) detected (e.g. test_config.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://otava.apache.org/docs/overview
  • Detailed PyPI description (3250 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 159 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 12 unique contributor(s) across 100 commits in apache/otava
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • try: with socket.create_connection(("localhost", host_port), timeout=1):
  • amp}\n" try: with socket.create_connection(("localhost", carbon_port), timeout=5) as sock:
  • ) data_str = urllib.request.urlopen(url).read() data_as_json = json.loads(da
  • ) data_str = urllib.request.urlopen(url).read() data_as_json = json.loads(da
  • efix}" data_str = urllib.request.urlopen(url).read() data_as_json = json.loads(da
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • command, "--help"] return subprocess.run( cmd, stdout=subprocess.PIPE, stderr
  • local.sample"] proc = subprocess.run( cmd, cwd=str(td_path),
  • anch", "main"] proc = subprocess.run( cmd, cwd=str(td_path),
  • , "feature-x"] proc = subprocess.run( cmd, cwd=str(td_path),
  • the container proc = subprocess.run(cmd, capture_output=True, text=True, timeout=60) if
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: otava.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/otava appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with apache-otava 
Develop a real-time performance monitoring tool using the 'apache-otava' package in Python. This tool will serve as a mini-application designed to help developers and system administrators detect changes in the performance of their applications over time. The application should be able to collect metrics from various sources such as logs, database queries, and API endpoints, and then analyze these metrics to identify any anomalies or significant changes that might indicate a performance issue or improvement.

Step 1: Set up your development environment with Python and install the 'apache-otava' package along with any other necessary dependencies like requests for HTTP calls and pandas for data manipulation.

Step 2: Design a simple user interface that allows users to input the URLs of the services they want to monitor and specify the types of metrics they're interested in (e.g., response times, error rates).

Step 3: Implement a background process that periodically fetches the specified metrics from the monitored services. Use 'apache-otava' to perform change detection on these metrics. Configure 'apache-otava' to alert the user if it detects a significant change in performance that could indicate a problem or an improvement.

Step 4: Integrate a logging mechanism into your application so that all detected changes and alerts are recorded. This log can be useful for auditing purposes or for generating reports on service performance over time.

Suggested Features:
- Customizable alert thresholds based on historical performance data.
- Support for multiple monitoring intervals (e.g., every 5 minutes, hourly, daily).
- A dashboard that visualizes the collected metrics and highlights recent changes detected by 'apache-otava'.
- Export functionality for logs and performance reports in formats such as CSV or PDF.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!