MEASURING THE RELIABILITY OF FREE PUBLIC APIS: A PYTHON-BASED EMPIRICAL STUDY - Maftunaxon G'ulomova; Ramziddin Khusanov (TADQIQOTLAR, 2026)
Abstract Third-party APIs have become a foundational component of modern software systems, enabling applications to access weather data, financial information, geographic lookups, and other services without building those capabilities from scratch. Despite their widespread adoption, little empirical data exists on the comparative reliability of free public APIs across different application domains. This paper presents a Python-based empirical study that simulates polling twelve publicly accessible APIs over a seven-day period, generating 24,192 observations. We analyse three key reliability dimensions: uptime rate, response latency (p50, p95, p99), and error type distribution. Our findings reveal statistically meaningful differences across API categories, with utility and geo APIs demonstrating consistently higher uptime (above 98%) and lower latency, while news and sports APIs exhibit the most degraded performance. Timeout events account for the majority of all failures regardless of category. These results offer developers a data-driven basis for selecting third-party APIs and highlight the importance of measuring p95 and p99 latency, rather than relying on mean response time alone.