asreview

v3.0.7 suspicious
5.0
Medium Risk

ASReview LAB - A tool for AI-assisted systematic reviews

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks for obfuscation and credential harvesting, but the metadata raises some concerns due to sparse author information and potential account inactivity.

  • Low obfuscation risk
  • Low credential risk
  • Sparse author information
  • Potential new or inactive account
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author information is sparse, and the account appears new or inactive, raising some suspicion.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://asreview.readthedocs.io/en/stable//
  • Detailed PyPI description (6666 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 6 type-annotated function signatures (partial)
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 9 unique contributor(s) across 100 commits in asreview/asreview
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • try: with socket.create_connection((host, port), timeout=1): return True
  • d and name response = requests.post( params["token_url"], data={
  • dress. response = requests.post( params["token_url"], data={
  • ta response = requests.get( f"https://pub{orcid_env}.orcid.org/v3.0
  • ["github"] response = requests.post( params["token_url"], data={
  • er profile response = requests.get( "https://api.github.com/user", head
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • asreview" / "webapp" subprocess.check_call( ["npm", "install"], cwd=str(path_we
  • "Windows"), ) subprocess.check_call( ["npm", "run-script", "build"], cwd
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: uu.nl>

  • Very short email domain: uu.nl>
✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository asreview/asreview 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 asreview 
Create a mini-application called 'LiteratureMiner' that leverages the ASReview LAB library to assist researchers in conducting systematic literature reviews more efficiently. This application should enable users to input their research question, keywords, and a list of databases from which they wish to gather articles. The application will then use ASReview's AI capabilities to sift through the vast amount of literature, identifying relevant studies based on the provided criteria. Here’s a detailed breakdown of the project steps and features:

1. **User Interface Design**: Develop an intuitive web interface where users can enter their research query, including specific keywords, exclusion criteria, and preferred databases.
2. **Data Collection**: Integrate ASReview’s data collection module to fetch papers from various academic databases such as PubMed, IEEE Xplore, and Google Scholar based on user inputs.
3. **AI-Assisted Review Process**: Utilize ASReview’s AI algorithms to prioritize papers for review. Users should be able to interactively label papers as relevant or irrelevant, feeding back into the model to improve its accuracy over time.
4. **Report Generation**: Implement a feature that compiles all reviewed papers into a structured report, highlighting key findings, trends, and gaps in the current literature based on the systematic review process.
5. **Customization Options**: Allow users to customize the AI model parameters, such as the machine learning algorithm used, to better suit their specific needs.
6. **Export Capabilities**: Provide options for exporting the final dataset and reports in common formats like CSV, PDF, and JSON.

To achieve these functionalities, you'll need to leverage several key aspects of the ASReview package, including its data handling capabilities, interactive labeling system, and customizable AI models. Additionally, consider integrating visualization tools within the application to help users understand the distribution and relevance of the collected papers.

💬 Discussion Feed

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