The Case for a Third Test State
A common challenge in software development is accurately representing the outcome of automated checks. Traditionally, tests yield a binary result: pass or fail. However, this dichotomy often fails to capture the nuances of complex systems. A recent discussion on Dev.to, initiated by developer Mahir Hir, posits that a third state, 'UNKNOWN', is crucial for maintaining test suite integrity, particularly when dealing with dependency cycles.
Hir argues that folding ambiguous or indeterminate results into either 'pass' or 'fail' leads to a suite that is effectively lying. This lie, he contends, erodes trust in the testing process and can mask underlying issues. The core of his argument is that not every check can definitively return a true or false answer; some situations simply don't provide enough information for a conclusive judgment.
The author’s initial struggle was with dependency cycles. These occur when modules or packages in a software project depend on each other in a circular fashion. Such cycles can indicate poor architectural design and can lead to unpredictable behavior or build failures. Hir observed that his team’s system labelled detected dependency cycles as 'UNKNOWN', implying that the system could not determine a definitive outcome. He initially defended this as a form of 'rigour', suggesting that the inability to resolve the cycle without crashing was a nuanced outcome.
Dependency Cycles: Not an Unknown, but a Failure
The critical moment came when a colleague on Hir's own team challenged this classification. The argument was straightforward: a dependency cycle is not a situation where the system is unable to determine an outcome. Instead, it is a definitive, negative outcome identified through a completed traversal of the project's dependency graph. The graph was loaded, the traversal executed, and the conclusion was clear: the structure was not a Directed Acyclic Graph (DAG), which is typically a requirement for predictable software builds. Therefore, the result was not an 'unknown' but a 'FAIL'.
This distinction is crucial. An 'UNKNOWN' state, as Hir initially defined it, is reserved for scenarios where the instrument genuinely cannot decide. Examples include a human-generated record that cannot be re-run, a file that fails to open, or a rule whose input data never arrives. These are situations where the test or check itself is hampered by external factors or inherent limitations, preventing a definitive assessment. A dependency cycle, conversely, is a factual state of the project's architecture that the system *can* detect and classify. It represents a clear failure to meet a structural requirement.
The implication is that labelling a detected dependency cycle as 'UNKNOWN' is not rigorous; it's a misclassification that obscures a real problem. By assigning it 'UNKNOWN', the system implies a lack of information or an inability to process, rather than a direct violation of a build or architectural rule. This can lead developers to overlook or deprioritize fixing these cycles, assuming they are a non-critical anomaly rather than a structural defect.
The Broader Implications for Testing
Hir’s experience underscores a fundamental challenge in designing robust testing frameworks. The temptation to create flexible or lenient states can lead to a dilution of the testing system's authority. When a test suite is perceived as forgiving or prone to misclassification, its value diminishes. Developers may start to ignore warnings or failures that are not presented with absolute clarity and certainty.
The ideal testing system should provide actionable insights. A 'FAIL' for a dependency cycle, accompanied by details about the specific cycle detected (e.g., `reason=cycle ids=…`), is far more actionable than an 'UNKNOWN'. It tells developers exactly what the problem is and that it requires immediate attention. This binary outcome, when accurate, drives the necessary changes to architectural integrity.
This incident serves as a potent reminder that the labels we apply to test outcomes carry significant weight. They shape developer perception and influence the priority given to fixing issues. While the desire for nuanced testing outcomes is valid, especially in complex, dynamic systems, the chosen labels must accurately reflect the nature of the detected condition. Rigour in testing means not just detecting problems, but also clearly and accurately reporting them, allowing development teams to address them effectively. The debate initiated by Hir's post highlights that true rigour lies in precise classification, not in creating ambiguity where certainty is possible.
What This Means for Developers
Developers must critically evaluate the output of their CI/CD pipelines and testing frameworks. If a tool reports an 'UNKNOWN' state for a condition that appears to be a clear violation (like a dependency cycle), it's a red flag. This suggests the tool might be masking problems or that its classification logic needs refinement. Prioritizing fixes for issues clearly flagged as 'FAIL', with explicit reasons, is paramount for maintaining code health and architectural stability.
What This Means for Security Professionals
While this specific issue doesn't directly involve a security vulnerability, the principle of accurate reporting is critical. Inaccurate classifications in security scanning tools, for instance, can lead to a false sense of security or misallocation of resources. A dependency cycle, if exploited through a vulnerable component within the cycle, could open avenues for attack. Clear, unambiguous reporting of all detected issues, regardless of perceived severity, is essential for a robust security posture.
What This Means for Founders
For startup founders and engineering leads, this incident is a lesson in the importance of reliable development tooling. A testing suite that provides misleading or ambiguous results can slow down development, increase technical debt, and ultimately impact product quality and delivery timelines. Investing in tools that offer clear, actionable feedback, and fostering a culture that respects definitive test failures, is key to building a sustainable and efficient engineering organization.
What This Means for Creators
Creators building software, whether for personal projects or commercial products, rely on their tools to guide them. If a build system or linter mislabels a structural issue like a dependency cycle, it can lead to unexpected bugs or performance problems down the line. Understanding the precise meaning of each output from development tools ensures that creators can build more stable and predictable applications.
What This Means for Data Scientists
In data science, the concept of 'unknown' versus 'fail' is also relevant, particularly in data validation or model evaluation. If a data pipeline fails to process a subset of data due to an anomaly, labelling it 'UNKNOWN' might obscure the fact that the data itself is malformed or missing required fields. Similarly, if a model's performance metrics fall below a critical threshold, it's a 'FAIL', not an 'UNKNOWN' state, indicating a need for retraining or architectural changes.