I built an API traffic classifier for business workflows

An engineer developed a Python traffic classifier to infer business workflows directly from captured API traffic, aiming to reduce reliance on undocumented institutional knowledge. Using JWT subjects, request identifiers, session boundaries, state-changing calls, surrounding reads, and locally processed endpoint and field-name information, the tool analyzed a synthetic banking application’s traffic and identified recurring activities such as account overviews, deposits, transfers, registration, statement exports, and multiple transfer-verification variants. Initial approaches that matched exact endpoint sequences produced excessive, misleading workflow counts because retries, optional lookups, and reordered reads were treated as distinct behaviors; anchoring workflows on writes reduced this fragmentation substantially. The experiment also exposed classification errors involving ambiguous data formats, repetitive status text, and traffic accidentally merged from separate clusters, prompting changes to field-priority rules, text analysis, and environment-aware session keys. The author argues that comparing inferred workflows with existing tests could reveal and prioritize test coverage gaps, while acknowledging limitations involving unstructured text, non-JSON payloads, synthetic data, and cross-service workflows involving event systems.

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