SEJun 3, 2019

Empirical Analysis of Factors and their Effect on Test Flakiness - Practitioners' Perceptions

arXiv:1906.00673v121 citations
Originality Synthesis-oriented
AI Analysis

This addresses the issue of wasted developer time due to flaky tests for software practitioners, but it is incremental as it builds on prior research by focusing on perceptions rather than new detection methods.

The paper tackles the problem of test flakiness in software development by conducting a multiple case study across four industries in Scandinavia to understand practitioners' perceptions, identifying 23 factors affecting flakiness with an average agreement rate of 86% on factors and their effects.

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.

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