hypothesis-synthesizer

To turn log anomalies and user transcripts into a testable hypothesis, you synthesize multi-source behavioral metrics using triangulation. The process requires at least two agreeing signals to generate a structured If/Then/Because statement and a draft test name.

Triangulation in behavioral metrics and log analysis is the cross-referencing of log anomalies, transcript evidence, and behavioral indicators. It ensures a testable hypothesis reflects real user and system signals by requiring at least two of three sources to agree.

You create a deepeval test name from scattered evaluation signals by synthesizing log anomalies and behavioral metrics into a test-first output. This yields a structured If/Then/Because statement, a primary assertion focus, and the expected draft test name.

Hypothesis generation requires triangulation where at least two of three signals agree, so it will not work with behavioral metrics alone. You must provide multi-source signals, such as behavioral metrics combined with transcript evidence, to pass the evidence-based gating.

To plan reproducible evaluation sessions from multi-source user data, you synthesize the signals into a structured hypothesis that specifies expected pre-fix FAIL versus post-fix PASS outcomes. This approach suggests exact sessions to reproduce and measure.

You cannot use this approach to automatically propose fixes for log anomalies, as it focuses strictly on test-first output. Instead of proposing solutions, it produces a narrow If/Then/Because statement and a primary assertion to guide measurable changes.