Unhandled Events, Not Broken States, Are What Kill Your State Machine

Unhandled events, not broken states, are the true culprits that render state machines ineffective. In a typical test scenario, a state machine's legal paths may include transitions from "pending" to "paid," then to "shipped," and finally to "delivered." However, these tests offer little insight into the more prevalent failures that cripple services.

Missing transitions are far more problematic than incorrect transitions, as the latter at least triggers an exception that can be traced in logs. Conversely, undefined transitions often glide through to a default branch, silently returning the current state and reporting success.

This issue intensifies when the state machine is drafted by a model. While models excel at generating transitions from a natural language description, they seldom inquire about the appropriate response when an event like "cancel" occurs after an order has already shipped. Consequently, the grid remains incomplete, and developers frequently patch the gaps using broad "else" clauses, inadvertently allowing the state machine to deviate from the actual order history that customers rely on.

To address this, one must start from the contract rather than a vague request. The focus should be on identifying unhandled events rather than broken states. A robust test should confirm that every combination of state and event has an explicit owner, even if the owner's responsibility is to reject the event. This clarity is much more attainable when utilizing inexpensive computational power to enumerate all possible state-event pairs and leveraging a free model to suggest which combination should reject the event instead of handling it.

Monetate's product outreach provides a free model access and a free server option, enabling the examination of the entire state machine rather than just a handful of happy paths. However, the true value lies in forcing the model to differentiate between events that must be rejected for each state, rather than merely focusing on events that advance the machine forward. By leveraging the model's guesses to create a table, one can verify that the test accurately reflects the machine's behavior.

Consider a small state machine with a deliberately incomplete transition map. The real challenge lies in identifying the missing cells, which are not immediately apparent from reading the code. By enumerating the grid using itertools.product, developers can reveal the hidden gaps. For instance, combinations like (shipped, cancel), (delivered, cancel), and (refunded, pay) are often absent from the initial transition map.

These undefined transitions are not errors per se, but they are absent from the machine's vocabulary and may eventually arise in production traffic, leading to unexpected behavior.

To mitigate this risk, developers can utilize a free model to classify each undefined cell. The model should not arbitrarily assign a next state based on plausibility; instead, it should categorize each combination into one of two categories: an explicit target state or an explicit rejection. This distinction is crucial because a rejection is itself a decision.

If the model determines that a cancel event on a shipped order should leave the state unchanged, this information must be encoded as an assertion rather than relying on a default branch to handle the situation. By transforming the model's classification into a verifiable table, developers can ensure that the state machine adheres to its intended contract, thereby preventing unintended deviations from the expected order history.