Empirical Analysis of Factors and their Effect on Test Flakiness - Practitioners' Perceptions
June 03, 2019 Β· Declared Dead Β· π Software testing, verification & reliability
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Azeem Ahmad, Ola Leifler, Kristian Sandahl
arXiv ID
1906.00673
Category
cs.SE: Software Engineering
Citations
20
Venue
Software testing, verification & reliability
Last Checked
4 months ago
Abstract
Developers always wish to ensure that their latest changes to the code base do not break existing functionality. If test cases fail, they expect these failures to be connected to the submitted changes. Unfortunately, a flaky test can be the reason for a test failure. Developers spend time to relate possible test failures to the submitted changes only to find out that the cause for these failures is test flakiness. The dilemma of an identification of the real failures or flaky test failures affects developers' perceptions about what is test flakiness. Prior research on test flakiness has been limited to test smells and tools to detect test flakiness. In this paper, we have conducted a multiple case study with four different industries in Scandinavia to understand practitioners' perceptions about test flakiness and how this varies between industries. We observed that there are little differences in how the practitioners perceive test flakiness. We identified 23 factors that are perceived to affect test flakiness. These perceived factors are categorized as 1) Software test quality, 2) Software Quality, 3) Actual Flaky test and 4) Company-specific factors. We have studied the nature of effects such as whether factors increase, decrease or affect the ability to detect test flakiness. We validated our findings with different participants of the 4 companies to avoid biases. The average agreement rate of the identified factors and their effects are 86% and 86% respectively, among participants.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted