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.