abaudit

v0.1.2 safe
4.0
Medium Risk

Statistical validity auditor for A/B tests — because significant != trustworthy.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network or shell activity. While there is potential obfuscation, it's not strongly indicative of malicious intent given the nature of the package's functionality.

  • Low network and shell risk
  • Potential obfuscation but likely for functional reasons
  • No clear signs of credential misuse
Per-check LLM notes
  • Network: No network calls suggest the package does not engage in external communications, which is typical for most local utility tools.
  • Shell: No shell execution detected indicates the package does not run external commands, reducing risk of unexpected behavior or exploitation.
  • Obfuscation: The usage of dynamic imports and random number generation might indicate obfuscation but could also be legitimate for cryptographic or simulation purposes.
  • Credentials: No clear signs of credential harvesting or secret handling were detected.
  • Metadata: The maintainer has a new or inactive account with no author name provided, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • g is added.""" rng = __import__("numpy").random.default_rng(99) ctrl = rng.normal(0.0, 1.0,
  • iggered(self): rng = __import__("numpy").random.default_rng(0) ctrl = rng.normal(0.0, 1.0, 1
  • ndation(self): rng = __import__("numpy").random.default_rng(0) ctrl = rng.normal(0.0, 1.0, 1
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ucsd.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aldair-ai/abaudit 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 abaudit 
Develop a mini-application named 'ABTestInspector' using Python, which leverages the 'abaudit' package to ensure the statistical integrity of A/B test results. This tool aims to help marketers and data analysts quickly assess whether the differences observed in their A/B test outcomes are statistically significant and trustworthy. Here's a detailed breakdown of the steps and features your application should include:

1. **User Interface Design**: Create a simple yet intuitive command-line interface (CLI) where users can input their A/B test data.
2. **Data Input**: Users should be able to upload two sets of numerical data representing the control group and the experimental group of an A/B test.
3. **Statistical Analysis**: Utilize the 'abaudit' package to perform the following analyses:
   - Calculate the mean and standard deviation for both groups.
   - Conduct a t-test to determine if there is a statistically significant difference between the two groups.
   - Use abaudit's core functions to assess the robustness of the t-test results against potential biases or anomalies.
4. **Result Presentation**: Display the results of the statistical analysis in a clear format, including p-values, confidence intervals, and any warnings or notes about the reliability of the results from abaudit.
5. **Optional Feature - Visualization**: Implement an optional feature that generates visual representations of the data and analysis results (e.g., bar charts showing means, histograms of data distribution).
6. **Documentation**: Provide comprehensive documentation on how to use the CLI, including examples and explanations of the output.
7. **Testing**: Ensure thorough testing of the application to validate its functionality and accuracy, especially focusing on edge cases and large datasets.

This project not only enhances understanding of statistical methods but also promotes best practices in interpreting A/B test outcomes.