An official website of the United States government
Flaky tests are tests that can non-deterministically pass or fail for the same code version. These tests undermine regression testing efficiency, because developers cannot easily identify whether a test fails due to their recent changes or due to flakiness. Ideally, one would detect flaky tests right when flakiness is introduced, so that developers can then immediately remove the flakiness. Some software organizations, e.g., Mozilla and Netflix, run some tools—detectors—to detect flaky tests as soon as possible. However, detecting flaky tests is costly due to their inherent non-determinism, so even state-of-the-art detectors are often impractical to be used on all tests for each project change. To combat the high cost of applying detectors, these organizations typically run a detector solely on newly added or directly modified tests, i.e., not on unmodified tests or when other changes occur (including changes to the test suite, the code under test, and library dependencies). However, it is unclear how many flaky tests can be detected or missed by applying detectors in only these limited circumstances. To better understand this problem, we conduct a large-scale longitudinal study of flaky tests to determine when flaky tests become flaky and what changes cause them to become flaky. We apply two state-of-theart detectors to 55 Java projects, identifying a total of 245 flaky tests that can be compiled and run in the code version where each test was added. We find that 75% of flaky tests (184 out of 245) are flaky when added, indicating substantial potential value for developers to run detectors specifically on newly added tests. However, running detectors solely on newly added tests would still miss detecting 25% of flaky tests. The percentage of flaky tests that can be detected does increase to 85% when detectors are run on newly added or directly modified tests. The remaining 15% of flaky tests become flaky due to other changes and can be detected only when detectors are always applied to all tests. Our study is the first to empirically evaluate when tests become flaky and to recommend guidelines for applying detectors in the future.
more »
« less
Wait Wait. No, Tell Me. Analyzing Selenium Configuration Effects on Test Flakiness
Flaky tests are a source of frustration and uncertainty for developers. In an educational environment, flaky tests can create doubts related to software behavior and student grades, especially when the grades depend on tests passing. NC State University's junior-level software engineering course models industrial practice through team-based development and testing of new features on a large electronic health record (EHR) system, iTrust2. Students are expected to maintain and supplement an extensive suite of UI tests using Selenium WebDriver. Team builds are run on the course's continuous integration (CI) infrastructure. Students report, and we confirm, that tests that pass on one build will inexplicably fail on the next, impacting productivity and confidence in code quality and the CI system. The goal of this work is to find and fix the sources of flaky tests in iTrust2. We analyze configurations of Selenium using different underlying web browsers and timeout strategies (waits) for both test stability and runtime performance. We also consider underlying hardware and operating systems. Our results show that HtmlUnit with Thread waits provides the lowest number of test failures and best runtime on poor-performing hardware. When given more resources (e.g., more memory and a faster CPU), Google Chrome with Angular waits is less flaky and faster than HtmlUnit, especially if the browser instance is not restarted between tests. The outcomes of this research are a more stable and substantially faster teaching application and a recommendation on how to configure Selenium for applications similar to iTrust2 that run in a CI environment.
more »
« less
- PAR ID:
- 10100316
- Date Published:
- Journal Name:
- Proceedings of the 14th International Workshop on Automation of Software Test
- Page Range / eLocation ID:
- 7-13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Mutation testing is widely used in research as a metric for evaluating the quality of test suites. Mutation testing runs the test suite on generated mutants (variants of the code under test), where a test suite kills a mutant if any of the tests fail when run on the mutant. Mutation testing implicitly assumes that tests exhibit deterministic behavior, in terms of their coverage and the outcome of a test (not) killing a certain mutant. Such an assumption does not hold in the presence of flaky tests, whose outcomes can non-deterministically differ even when run on the same code under test. Without reliable test outcomes, mutation testing can result in unreliable results, e.g., in our experiments, mutation scores vary by four percentage points on average between repeated executions, and 9% of mutant-test pairs have an unknown status. Many modern software projects suffer from flaky tests. We propose techniques that manage flakiness throughout the mutation testing process, largely based on strategically re-running tests. We implement our techniques by modifying the open-source mutation testing tool, PIT. Our evaluation on 30 projects shows that our techniques reduce the number of "unknown" (flaky) mutants by 79.4%.more » « less
-
When developers make changes to their code, they typically run regression tests to detect if their recent changes (re)introduce any bugs. However, many tests are flaky, and their outcomes can change non-deterministically, failing without apparent cause. Flaky tests are a significant nuisance in the development process, since they make it more difficult for developers to trust the outcome of their tests. The traditional approach to identify flaky tests is to rerun them multiple times: if a test is observed both passing and failing on the same code, it is definitely flaky. We conducted a very large empirical study looking for flaky tests by rerunning the test suites of 24 projects 10,000 times each, and found that even with this many reruns, some flaky tests were still not detected. We propose FlakeFlagger, a novel approach that collects a set of features describing the behavior of each test, and then predicts tests that are likely to be flaky based on similar behavioral features. We found that FlakeFlagger correctly labeled at least as many tests as flaky as a state-of-the-art flaky test classifier, but that FlakeFlagger reported far fewer false positives (an increase in precision from just 11% to 60%). This lower false positive rate translates directly to saved time for researchers and developers who use the classification result to guide more expensive flaky test detection processes. By investigating the information gain of each feature, we conclude that test execution time, overall test coverage, coverage of recently changed lines and usage of third party libraries are effective predictors of test flakiness. We did not find any keywords or tokens in the source code of tests that were effective in predicting test flakiness, and did not find the presence of test smells to be effective in predicting test flakiness. This archive contains the dataset that we collected of flaky tests, along with the features that we collected from each test. Contents: Project_Info.csv: List of projects and their revisions studied build-logs-<project-slug>.tgz: An archive of all of the maven build logs from each of the 10,000 runs of that project's test suite. failing-test-reports-<project-slug>.tgz An archive of all of the surefire XML reports for each failing test of each build of each project. test_results.csv: Summary of the number of passing and failing runs for each test in each project. "Run ID" is a key into the <project-slug>.tgz archive also in this artifact, which refers to the run that we observed the test fail on. test_features.csv: Summary of the features that each test had, as per our feature detectors described in the paper flakeflagger-code.zip: All scripts used to generate and process these results. These scripts are also located at https://github.com/AlshammariA/FlakeFlaggermore » « less
-
Regression testing is increasingly important with the wide use of continuous integration. A desirable requirement for regression testing is that a test failure reliably indicates a problem in the code under test and not a false alarm from the test code or the testing infrastructure. However, some test failures are unreliable, stemming from flaky tests that can non- deterministically pass or fail for the same code under test. There are many types of flaky tests, with order-dependent tests being a prominent type. To help advance research on flaky tests, we present (1) a framework, iDFlakies, to detect and partially classify flaky tests; (2) a dataset of flaky tests in open-source projects; and (3) a study with our dataset. iDFlakies automates experimentation with our tool for Maven-based Java projects. Using iDFlakies, we build a dataset of 422 flaky tests, with 50.5% order-dependent and 49.5% not. Our study of these flaky tests finds the prevalence of two types of flaky tests, probability of a test-suite run to have at least one failure due to flaky tests, and how different test reorderings affect the number of detected flaky tests. We envision that our work can spur research to alleviate the problem of flaky tests.more » « less
-
null (Ed.)Assertions are widely used for functional validation as well as coverage analysis for both software and hardware designs. Assertions enable runtime error detection as well as faster localization of errors. While there is a vast literature on both software and hardware assertions for monitoring functional scenarios, there is limited effort in utilizing assertions to monitor System-on-Chip (SoC) security vulnerabilities. We have identified common SoC security vulnerabilities and defined several classes of assertions to enable runtime checking of security vulnerabilities. A major challenge in assertion-based validation is how to activate the security assertions to ensure that they are valid. While existing test generation using model checking is promising, it cannot generate directed tests for large designs due to state space explosion. We propose an automated and scalable mechanism to generate directed tests using a combination of symbolic execution and concrete simulation of RTL models. Experimental results on diverse benchmarks demonstrate that the directed tests are able to activate security assertions non-vacuously.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/AST.2019.000-1
