iDFlakies: A Framework for Detecting and Partially Classifying Flaky Tests
| Autor: | Darko Marinov, Wing Lam, Reed Oei, August Shi, Tao Xie |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Test failure
Computer science 020207 software engineering 02 engineering and technology Open source software computer.software_genre Test (assessment) Continuous integration Test code 020204 information systems Regression testing 0202 electrical engineering electronic engineering information engineering Data mining False alarm computer Reliability (statistics) |
| Zdroj: | ICST |
| Popis: | 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 nondeterministically 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. |
| Databáze: | OpenAIRE |
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