Why Autogenerated Unit Tests Can Be An Anti-Pattern

#unittest #softwaretest #bestpractices #genai

We are all excited about the potential of Generative AI (GenAI) to boost our productivity. However, it's crucial to critically evaluate where and how we apply these tools, especially in areas like software testing. This post explores why relying solely on GenAI to generate unit tests can be an anti-pattern, ultimately undermining the very purpose of testing.

The Core Purpose of Unit Tests

Unit tests are about verifying that the code we write aligns with our intended design and discover bugs as early as possible. They force us, as developers, to clearly articulate the behavior we expect from a specific unit of code. To focus purely on the implementation details, we strategically mock dependencies. This isolation allows us to confidently assess if the code behaves as we designed it—a critical part of the development process. It's a feedback loop that solidifies understanding.

The Illusion of Green: Why GenAI-Generated Tests Can Be Misleading

Let's assume, for a moment, that a GenAI LLM can consistently produce unit tests that compile and result in a "green" build status. Does this mean our code is bug-free? ** Absolutely not *. It simply means the LLM has crafted tests that *pass, regardless of underlying issues.

Here’s where the real problem lies:

Testing the Tests, Not the Code: If we discover a bug and fix it, the GenAI-generated test will likely fail if by chance the assertion is well crafted. The natural (and dangerously flawed) response might be to simply rerun the LLM on the corrected code to regenerate a passing test. This cycle demonstrates that the tests are validating the LLM's ability to create passing tests, not the correctness of our code. They offer a false sense of security.
Weak Assertions: GenAI-generated tests often feature weak or overly general assertions. These assertions have a high probability of "sticking" even when the underlying code's behavior is flawed. It’s easy to pass a test that doesn't truly validate the logic. See below for an example.
Erosion of Trust: Historically, a suite of passing unit tests has signaled a degree of code quality and reliability. When that signal is diluted by tests designed to pass despite bugs, we lose a valuable indicator of quality and increase risk.
See an example of source code here (Source code):

See LLM generated test here:

We observe the following:

By default, many crucial code paths are not adequately exercised, leading to a false sense of security indicated by a “green” build status.
The test assertion merely verifies the presence of certain fields within a data structure, without validating the correctness of their values. This constitutes a very weak and insufficient test.

Below is another source code:

Generated test is:

We observe the following:

The test case for the test_str variable expects a Markdown-encoded table. The LLM’s failure to generate a valid test highlights a lack of clarity in the original code – valuable feedback if the generated tests are reviewed! However, without human review, this important insight would be lost.
The assertion remains weak, providing limited validation of the code’s behavior.

Is GenAI Useless for Unit Testing? Not Entirely.

Despite these concerns, GenAI isn't without value in the unit testing workflow. The most promising application lies in human-LLM collaboration:

Boilerplate Generation: LLMs excel at generating the structural boilerplate of unit tests – setting up mocks, creating test methods, and ensuring comprehensive code coverage. This can significantly reduce the tedious and repetitive aspects of test creation, combating "test fatigue."
Coverage Focus: An LLM can quickly identify untested code paths, helping ensure maximum code coverage with the framework for the test.

However, the critical step remains with the developer: We must write the assertions. We must define the specific, meaningful validations that confirm the code behaves as intended. The LLM prepares the ground; the developer ensures the seeds of quality are sown.

Below is an example how such a human LLM collaboration could happen:

Following this schema, we begin by generating an initial set of unit tests. We then measure code coverage and compare the uncovered code paths—as identified by analysis of the Abstract Syntax Tree—to those exercised by the existing tests. This information is used to prompt the LLM to generate additional tests, specifically designed to activate the missing paths with appropriate parameter values.

We consolidate each new set of tests and remeasure code coverage, repeating this process recursively until 100% coverage is achieved.

Crucially, human intervention is then applied to craft robust and meaningful assertions for each test path, ensuring that each test genuinely validates the intended behavior.

Conclusion

While GenAI offers enticing possibilities, blindly accepting autogenerated unit tests as a substitute for thoughtful, developer-driven testing is a mistake. Let’s leverage GenAI to assist us in writing better tests, not to replace the critical thinking and validation that are essential to building robust and reliable software.