In the world of software development and operations, a 'Flaky Test' is a term that carries significant implications. It refers to a type of automated test that behaves inconsistently, sometimes passing and sometimes failing, even when there are no changes in the code that it is testing. This inconsistency can lead to confusion, wasted time, and a lack of confidence in the testing process.
Flaky tests are a common issue in the DevOps world, where continuous integration and continuous delivery (CI/CD) practices are prevalent. In this context, flaky tests can become a major roadblock, as they can cause delays in the delivery pipeline and create uncertainty about the stability of the software product. This article will delve into the intricacies of flaky tests, their causes, impacts, and strategies for dealing with them.
Definition of Flaky Test
A flaky test is an automated test that exhibits non-deterministic behavior. This means that it can produce different results (pass or fail) in different runs, even when the code being tested has not changed. This unpredictability makes flaky tests a source of frustration for developers and testers alike, as they can obscure real issues and lead to false alarms.
Flaky tests can occur in any type of testing, including unit tests, integration tests, and end-to-end tests. They can be caused by a variety of factors, such as timing issues, dependencies on external systems, and non-deterministic algorithms. However, regardless of the cause, the result is the same: a test that cannot be relied upon to consistently report the state of the system under test.
Characteristics of Flaky Tests
Flaky tests are characterized by their unpredictability. They can pass or fail at random, without any apparent reason. This unpredictability can make it difficult to diagnose and fix the underlying issues, as the test results do not provide a consistent picture of the system's behavior.
Another characteristic of flaky tests is their tendency to 'hide' real issues. Because flaky tests can fail for reasons unrelated to the code being tested, they can lead to false positives that distract from real issues. This can result in wasted time and resources, as developers chase down phantom bugs that don't actually exist.
Types of Flaky Tests
Flaky tests can be categorized into two main types: those that are inherently flaky, and those that become flaky due to external factors. Inherently flaky tests are those that are designed in a way that makes them non-deterministic. For example, a test that relies on a random number generator is inherently flaky, as its outcome will vary with each run.
Tests that become flaky due to external factors are those that are affected by conditions outside the control of the test itself. For example, a test that depends on a remote server may become flaky if the server is slow or unavailable. Similarly, a test that depends on the system clock may become flaky if the clock is not synchronized accurately.
Causes of Flaky Tests
Understanding the causes of flaky tests is crucial for preventing them and dealing with them effectively. There are many potential causes of flaky tests, ranging from issues with the test design to problems with the environment in which the tests are run.
One common cause of flaky tests is timing issues. For example, a test may fail if it assumes that a certain operation will complete within a specific time frame, but the operation takes longer than expected. This can be particularly problematic in distributed systems, where network latency and other factors can introduce variability in operation times.
Dependency on External Systems
Another common cause of flaky tests is dependencies on external systems. If a test relies on a remote server, a database, or any other external system, it can become flaky if that system is not consistently available or behaves unpredictably. For example, a test that verifies the functionality of a web service may fail if the service is temporarily unavailable.
Even when external systems are available, they can still cause flaky tests if they do not behave consistently. For example, a test that verifies the functionality of a database may fail if the database returns results in a different order than expected. This can happen if the database does not guarantee a specific order of results, or if the order is affected by factors outside the control of the test.
Non-Deterministic Algorithms
Flaky tests can also be caused by non-deterministic algorithms. These are algorithms that can produce different results in different runs, even when the input is the same. For example, a test that verifies the functionality of a random number generator is inherently flaky, as the outcome will vary with each run.
While non-deterministic algorithms are sometimes necessary, they should be used sparingly in tests. If a test must use a non-deterministic algorithm, it should be designed in a way that minimizes the impact of the non-determinism on the test results. For example, the test could use a fixed seed for the random number generator, so that the sequence of numbers is the same in each run.
Impact of Flaky Tests
The impact of flaky tests can be significant, particularly in a DevOps context where automated testing is a key part of the delivery pipeline. Flaky tests can cause delays in the delivery of software, waste valuable resources, and undermine confidence in the testing process.
One of the most immediate impacts of flaky tests is the time and effort required to investigate and resolve the false alarms they generate. When a test fails, it typically triggers a process of investigation to determine the cause of the failure. If the failure is due to a flaky test, this investigation can be a waste of time and resources.
Delay in Software Delivery
Flaky tests can also cause delays in the delivery of software. In a DevOps context, tests are often run as part of a continuous integration (CI) process, where code changes are integrated and tested frequently. If a test fails, the CI process may be halted until the cause of the failure is resolved. If the failure is due to a flaky test, this can result in unnecessary delays.
Even when flaky tests do not halt the CI process, they can still cause delays by creating uncertainty about the stability of the software. If a test fails intermittently, it can be difficult to know whether a particular code change is safe to deploy. This uncertainty can lead to delays in deployment, as developers and testers may need to spend extra time verifying the stability of the software.
Undermining Confidence in Testing
Perhaps the most insidious impact of flaky tests is the way they can undermine confidence in the testing process. If tests fail intermittently and unpredictably, it can be difficult to trust the results they produce. This lack of trust can lead to a disregard for test results, which can in turn lead to real issues being overlooked.
Furthermore, flaky tests can create a 'boy who cried wolf' scenario, where frequent false alarms desensitize developers and testers to test failures. If a test is known to be flaky, its failures may be ignored or dismissed without investigation. This can lead to real issues being missed, as developers and testers become accustomed to ignoring the test results.
Dealing with Flaky Tests
Given the significant impact of flaky tests, it is crucial to have strategies for dealing with them. These strategies can be broadly categorized into two types: prevention and mitigation. Prevention strategies aim to avoid the creation of flaky tests in the first place, while mitigation strategies aim to minimize the impact of flaky tests that already exist.
Prevention strategies include good test design practices, such as avoiding dependencies on external systems, using deterministic algorithms, and accounting for timing variability. Mitigation strategies include techniques for identifying and isolating flaky tests, as well as practices for dealing with test failures.
Prevention Strategies
One of the most effective ways to prevent flaky tests is to design tests in a way that minimizes non-determinism. This can involve avoiding dependencies on external systems, using deterministic algorithms, and accounting for timing variability. For example, if a test must interact with a database, it could use a local, in-memory database instead of a remote one, to avoid network variability. Similarly, if a test must use a random number generator, it could use a fixed seed to ensure that the sequence of numbers is the same in each run.
Another prevention strategy is to use mocking and stubbing techniques to isolate the system under test from external factors. For example, a test that verifies the functionality of a web service could use a mock web service instead of the real one, to ensure that the test results are not affected by the availability or behavior of the real service. Similarly, a test that verifies the functionality of a database could use a stub database that returns predetermined results, to ensure that the test results are not affected by the actual state of the database.
Mitigation Strategies
If flaky tests already exist, there are several strategies that can be used to mitigate their impact. One of these is to identify and isolate flaky tests, so that their failures do not disrupt the rest of the testing process. This can be done by running the tests multiple times and observing their behavior, or by using tools that automatically detect flaky tests.
Once flaky tests have been identified, they can be isolated in a separate test suite, so that their failures do not halt the continuous integration process. This allows the rest of the tests to run uninterrupted, while the flaky tests can be investigated and fixed separately.
Conclusion
Flaky tests are a common issue in the DevOps world, where continuous integration and continuous delivery practices are prevalent. They can cause significant problems, including delays in software delivery, wasted resources, and a lack of confidence in the testing process. However, with good test design practices and effective mitigation strategies, it is possible to minimize the impact of flaky tests and maintain a reliable and efficient testing process.
By understanding the causes and impacts of flaky tests, and by implementing strategies for dealing with them, developers and testers can ensure that their testing process is robust, reliable, and effective. This can lead to faster delivery of software, more efficient use of resources, and greater confidence in the quality of the software product.