MockSniffer: Characterizing and Recommending Mocking Decisions for Unit Tests

Zhu，Hengcheng1; Wei，Lili2; Wen，Ming3; Liu，Yepang1; Cheung，Shing Chi2; Sheng，Qin4; Zhou，Cui4

10.1145/3324884.3416539

2020-09-01

1938-4300

978-1-7281-7281-1

Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020

436-447

21-25 Sept. 2020

Melbourne, VIC, Australia

In unit testing, mocking is popularly used to ease test effort, reduce test flakiness, and increase test coverage by replacing the actual dependencies with simple implementations. However, there are no clear criteria to determine which dependencies in a unit test should be mocked. Inappropriate mocking can have undesirable consequences: under-mocking could result in the inability to isolate the class under test (CUT) from its dependencies while over-mocking increases the developers' burden on maintaining the mocked objects and may lead to spurious test failures. According to existing work, various factors can determine whether a dependency should be mocked. As a result, mocking decisions are often difficult to make in practice. Studies on the evolution of mocked objects also showed that developers tend to change their mocking decisions: 17% of the studied mocked objects were introduced sometime after the test scripts were created and another 13% of the originally mocked objects eventually became unmocked. In this work, we are motivated to develop an automated technique to make mocking recommendations to facilitate unit testing. We studied 10, 846 test scripts in four actively maintained open-source projects that use mocked objects, aiming to characterize the dependencies thatare mocked in unit testing. Based on our observations on mocking practices, we designed and implemented a tool, MockSniffer, to identify and recommend mocks for unit tests. The tool is fully automated and requires only the CUT and its dependencies as input. It leverages machine learning techniques to make mocking recommendations by holistically considering multiple factors that can affect developers' mocking decisions. Our evaluation of Mock-Sniffer on ten open-source projects showed that it outperformed three baseline approaches, and achieved good performance in two potential application scenarios.

dependencies Mocking recommendation system unit testing

第一 ; 通讯

英语

EI ; CPCI-S

WOS:000651313500038

20210309773286

Automation ; Learning systems ; Open source software

Computer Software, Data Handling and Applications:723 ; Automatic Control Principles and Applications:731

2-s2.0-85099230324

Scopus

1.Southern University of Science and Technology,Shenzhen,China
2.Hong Kong University of Science and Technology,Hong Kong,Hong Kong
3.Huazhong University of Science and Technology,Wuhan,China
4.WeBank Co Ltd,Shenzhen,China

Zhu，Hengcheng,Wei，Lili,Wen，Ming,et al. MockSniffer: Characterizing and Recommending Mocking Decisions for Unit Tests[C],2020:436-447.