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Mousetraps: Reliably distinguishing free apps and trial versions of paid macOS apps on public app marketplaces

    Paper
  • Software Analysis

Transparency and clear labeling of macOS applications in public marketplaces are essential for fostering user trust and enabling informed decision-making.

This research focuses on distinguishing truly free applications from those containing in-app purchases and classifying them into free, freemium, and paid categories. The analysis combines technical and language-based approaches to identify key indicators of each class, followed by classification using a supervised machine learning algorithm.

Among the evaluated models, Random Forest and Histogram-Based Gradient Boosting demonstrated the highest performance, each achieving 90% precision, 90% recall, 89% F1-score, and 90% accuracy.

These findings support the development of automated app classification systems and hold practical value for enhancing transparency within the software distribution ecosystem.

@unpublished{litvinchuk-etal-2025-report-macos-paid-apps-analysis,
  author = {Zakhar Litvinchuk and Oleksandr Frankiv and Yevhenii Peteliev and Sergii Kryvoblotskyi and Nataliia Stulova},
  title  = {Mousetraps: Reliably distinguishing free apps and trial versions of paid macOS apps on public app marketplaces},
  note   = {Available online at \url{https://research.macpaw.com/publications/mousetraps}},
  month  = {May},
  year   = {2025}
}

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