Rafael Rampim Soratto
- 2018. International Conference on Software Engineering (Gothenburg, Suécia).Google Scholar
- 2018. International Working Conference on Source Code Analysis and Manipulation (Madrid, Espanha).Google Scholar
- 2021. International Conference on Software Engineering (Madrid, Espanha).Google Scholar
- Abdulrahman Alshammari, Christopher Morris, Michael Hilton, and Jonathan Bell. 2021. FlakeFlagger: Predicting Flakiness Without Rerunning Tests, See pro [3], 1572–1584.Google Scholar
- Gabriele Bavota, Abdallah Qusef, Rocco Oliveto, Andrea De Lucia, and David Binkley. 2012. An empirical analysis of the distribution of unit test smells and their impact on software maintenance. In 28th IEEE International Conference on Software Maintenance (Trento,Italy). 56–65.Google ScholarDigital Library
- Jonathan Bell, Owolabi Legunsen, Michael Hilton, Lamyaa Eloussi, Tifany Yung, and Darko Marinov. 2018. DeFlaker: Automatically Detecting Flaky Tests, See pro [1], 433–444.Google Scholar
- Jonathan Bell, Owolabi Legunsen, Michael Hilton, Lamyaa Eloussi, Tifany Yung, and Darko Marinov. 2018. DeFlaker: Automatically Detecting Flaky Tests, See pro [1], 433–444.Google Scholar
- Antonia Bertolino, Emilio Cruciani, Breno Alexandro Ferreira de Miranda, and Roberto Verdecchia. 2020. Know your neighbor: fast static prediction of test flakiness. techreport ISTI-2020-TR/001. Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Pisa, Itália.Google Scholar
- Bruno Camara, Marco Silva, Andre Endo, and Silvia Vergilio. 2021. On the Use of Test Smells for Prediction of Flaky Tests. In VI Brazilian Symposium on Systematic and Automated Software Testing (SAST’21) (6 ed.) (Joinville, SC, Brasil). 46–54.Google Scholar
- Bruno Henrique Pachulski Camara, Marco Aurélio Graciotto Silva, Andre Takeshi Endo, and Silvia Regina Vergilio. 2021. What is the Vocabulary of Flaky Tests? An Extended Replication. In 29th IEEE/ACM International Conference on Program Comprehension (ICPC 2021) (Madrid, Espanha). 444–454.Google Scholar
- David Cournapeau 2007. scikit-learn. Programa de computador. https://scikit-learn.org/Google Scholar
- Cypress. 2017. Cypress. Programa de computador. https://www.cypress.io/Google Scholar
- Arie Van Deursen, Leon Moonen, Alex Bergh, and Gerard Kok. 2001. Refactoring Test Code. In 2nd International Conference on Extreme Programming and Flexible Processes in Software Engineering (XP 2001) (Villasimius, Sardinia, Itália). 92–95.Google Scholar
- Moritz Eck, Fabio Palomba, Marco Castelluccio, and Alberto Bacchelli. 2019. Understanding Flaky Tests: The Developer’s Perspective. In 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Tallinn, Estônia). 830–840.Google Scholar
- Lamyaa Eloussi. 2015. Determining Flaky Tests from Test Failures. mathesis. University of Illinois at Urbana-Champaign, Urbana, Illinois,. Advisor(s) Darko Marinov. http://hdl.handle.net/2142/78543Google Scholar
- Facebook. 2014. Jest. Programa de computador. https://jestjs.io/Google Scholar
- Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. 1996. From Data Mining to Knowledge Discovery in Databases. AI Magazine 17, 3 (Sept.–Nov. 1996), 37–54.Google ScholarDigital Library
- Martin Gruber, Stephan Lukasczyk, Florian Kroiß, and Gordon Fraser. 2021. An Empirical Study of Flaky Tests in Python. In 2021 14th IEEE International Conference on Software Testing, Verification and Validation (Porto de Galinhas, PE, EUA). 148–158.Google Scholar
- Guillaume Haben, Sarra Habchi, Mike Papadakis, Maxime Cordy, and Yves Le Traon. 2021. A Replication Study on the Usability of Code Vocabulary in Predicting Flaky Tests. In 2021 Mining Software Repositories Conference (18 ed.) (Madrid, Espanha). 219–229.Google ScholarCross Ref
- Mark Harman and Peter O’Hearn. 2018. From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis, See pro [2], 1–23.Google Scholar
- Mark Harman and Peter O’Hearn. 2018. From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis, See pro [2], 1–23.Google Scholar
- Negar Hashemi, Amjed Tahir†, and Shawn Rasheed. 2022. An empirical study of flaky tests in Javascript. In 38th IEEE International Conference on Software Maintenance and Evolution (38 ed.) (Limassol, Chipre). 24–34.Google ScholarCross Ref
- James Henry 2019. TypeScript-ESLint. Programa de computador. https://github.com/typescript-eslint/typescript-eslintGoogle Scholar
- Kim Herzig and Nachiappan Nagappan. 2015. Empirically Detecting False Test Alarms Using Association Rules. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (37 ed.) (Florence, Itália). 39–48.Google Scholar
- Vojtech Jina 2012. Karma. Programa de computador. https://karma-runner.github.ioGoogle Scholar
- Tariq M. King, Dionny Santiago, Justin Phillips, and Peter J. Clarke. 2018. Towards a Bayesian Network Model for Predicting Flaky Automated Tests. In 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) (Lisbon, Portugal). 100–107.Google ScholarCross Ref
- Wing Lam, Patrice Godefroid, Suman Nath, Anirudh Santhiar, and Suresh Thummalapenta. 2019. Root causing flaky tests in a large-scale industrial setting. In 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. 101–111.Google ScholarDigital Library
- Wing Lam, Patrice Godefroid, Suman Nath, Anirudh Santhiar, and Suresh Thummalapenta. 2019. Root Causing Flaky Tests in a Large-Scale Industrial Setting. In 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (Beijing, China). 101–111.Google ScholarDigital Library
- Wing Lam, Kıvanç Muşlu, Hitesh Sajnani, and Suresh Thummalapenta. 2020. A Study on the Lifecycle of Flaky Tests. In 42nd International Conference on Software Engineering (Seoul, Coréia do Sul). 1471–1482.Google ScholarDigital Library
- Wing Lam, Reed Oei, August Shi, Darko Marinov, and Tao Xie. 2019. iDFlakies: A Framework for Detecting and Partially Classifying Flaky Tests. In 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) (Xi’an, China). 312–322.Google ScholarCross Ref
- Jeff Listfield. 2017. Where do our flaky tests come from?Página Web. https://testing.googleblog.com/2017/04/where-do-our-flaky-tests-come-from.htmlGoogle Scholar
- Qingzhou Luo, Farah Hariri, Lamyaa Eloussi, and Darko Marinov. 2014. An Empirical Analysis of Flaky Tests. In 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (Hong Kong, China). 643–653.Google Scholar
- Meta. 2016. Yarn. Programa de computador. https://yarnpkg.com/Google Scholar
- John Micco. 2016. Flaky tests at Google and how we mitigate them. Online] https://testing. googleblog. com/2016/05/flaky-tests-at-google-and-how-w e. html (2016).Google Scholar
- John Micco. 2017. The State of Continuous Integration Testing@ Google.(2017).Google Scholar
- Charles Miranda, Guilherme Avelino, Pedro Santos Neto, and Victor da Silva. 2021. Uma Análise da Co-Evolução de Teste em Projetos de Software no GitHub. In IX Workshop de Visualização, Evolução e Manutenção de Software (VEM 2021) (12 ed.) (Joinville, SC, Brasil). 36–40.Google Scholar
- Jesús Morán, Cristian Augusto, Antonia Bertolino, Claudio De La Riva, and Javier Tuya. 2020. FlakyLoc: Flakiness Localization for Reliable Test Suites in Web Applications. Journal of Web Engineering 19, 2 (June 2020), 267–296.Google Scholar
- NPM. 2010. npm. Programa de computador. https://www.npmjs.com/Google Scholar
- Jason Palmer. 2019. Test Flakiness – Methods for identifying and dealing with flaky tests. https://engineering.atspotify.com/2019/11/18/test-flakiness-methods-for-identifying-and-dealing-with-flaky-tests/Google Scholar
- Gustavo Pinto, Breno Miranda, Supun Dissanayake, Marcelo d’Amorim, Christoph Treude, and Antonia Bertolino. 2020. What is the Vocabulary of Flaky Tests?. In 17th International Conference on Mining Software Repositories (MSR) (17 ed.) (Seoul, Coréia do Sul). 492–502.Google ScholarDigital Library
- Tom Preston-Werner, Chris Wanstrath, P. J. Hyett, and Scott Chacon. 2008. GitHub. Programa de computador. https://github.comGoogle Scholar
- Solange Oliveira Rezende. 2005. Sistemas inteligentes: fundamentos e aplicações (1 ed.). Barueri, SP, Brasil. 550 pages.Google Scholar
- Alan Romano, Zihe Song, Sampath Grandhi, Wei Yang, and Weihang Wang. 2021. An Empirical Analysis of UI-based Flaky Tests, See pro [3], 1585–1597.Google Scholar
- Swapna Thorve, Chandani Sreshtha, and Na Meng. 2018. An Empirical Study of Flaky Tests in Android Apps. In 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME) (Madrid, Espanha). 534–538.Google Scholar
- Michele Tufano, Fabio Palomba, Gabriele Bavota, Massimiliano Di Penta, Rocco Oliveto, Andrea De Lucia, and Denys Poshyvanyk. 2016. An Empirical Investigation into the Nature of Test Smells. In 31st IEEE/ACM International Conference on Automated Software Engineering (Singapura). 4–15.Google ScholarDigital Library
- Roberto Verdecchia, Emilio Cruciani, Breno Miranda, and Antonia Bertolino. 2021. Know You Neighbor: Fast Static Prediction of Test Flakiness. IEEE Access (2021).Google Scholar
Index Terms
- Vocabulary of Flaky Tests in Javascript
Index terms have been assigned to the content through auto-classification.
Recommendations
Systematically Producing Test Orders to Detect Order-Dependent Flaky Tests
ISSTA 2023: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and AnalysisSoftware testing suffers from the presence of flaky tests, which can pass or fail when run on the same version of code. Order- dependent tests (OD tests) are flaky tests whose outcome depends on the order in which they are run. An OD test can be ...
A Survey of Flaky Tests
Tests that fail inconsistently, without changes to the code under test, are described as flaky. Flaky tests do not give a clear indication of the presence of software bugs and thus limit the reliability of the test suites that contain them. A recent ...
Mitigating the effects of flaky tests on mutation testing
ISSTA 2019: Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and AnalysisMutation 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 ...
Comments