Reference : PEELER: Learning to Effectively Predict Flakiness without Running Tests

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/52227

Title :	PEELER: Learning to Effectively Predict Flakiness without Running Tests
Language :	English
Author, co-author :	Qin, Yihao [National University of Defense Technology, China]
	Wang, Shangwen [National University of Defense Technology, China]
	Liu, Kui [Huawei Software Engineering Application Technology Lab, China]
	Lin, Bo [National University of Defense Technology]
	Wu, Hongjun [National University of Defense Technology, China]
	Li, Li [Monash University, Australia]
	Mao, Xiaoguang [National University of Defense Technology, China]
	Bissyande, Tegawendé François D Assise [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Publication date :	Oct-2022
Main document title :	Proceedings of the 38th IEEE International Conference on Software Maintenance and Evolution
Pages :	1-12
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	38th IEEE International Conference on Software Maintenance and Evolution
Event date :	from 02-10-2022 to 07-10-2022
Event organizer :	IEEE
Event place (city) :	Limassol
Event country :	Cyprus
Keywords :	[en] Flaky tests ; Deep learning ; Program dependency
Abstract :	[en] —Regression testing is a widely adopted approach to expose change-induced bugs as well as to verify the correctness/robustness of code in modern software development settings. Unfortunately, the occurrence of flaky tests leads to a significant increase in the cost of regression testing and eventually reduces the productivity of developers (i.e., their ability to find and fix real problems). State-of-the-art approaches leverage dynamic test information obtained through expensive re-execution of test cases to effectively identify flaky tests. Towards accounting for scalability constraints, some recent approaches have built on static test case features, but fall short on effectiveness. In this paper, we introduce PEELER, a new fully static approach for predicting flaky tests through exploring a representation of test cases based on the data dependency relations. The predictor is then trained as a neural network based model, which achieves at the same time scalability (because it does not require any test execution), effectiveness (because it exploits relevant test dependency features), and practicality (because it can be applied in the wild to find new flaky tests). Experimental validation on 17,532 test cases from 21 Java projects shows that PEELER outperforms the state-of-the-art FlakeFlagger by around 20 percentage points: we catch 22% more flaky tests while yielding 51% less false positives. Finally, in a live study with projects in-the-wild, we reported to developers 21 flakiness cases, among which 12 have already been confirmed by developers as being indeed flaky.
Permalink :	http://hdl.handle.net/10993/52227
Other URL :	https://www.researchgate.net/publication/361324187_Peeler_Learning_to_Effectively_Predict_Flakiness_without_Running_Tests/link/62aabeb123f3283e3aeadab7/download
Framework Programme / European Project :	H2020 ; 949014 - NATURAL - Natural Program Repair

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