SRTEF: Test Function Recommendation With Scenarios and Latent Semantic for Implementing Stepwise Test Case

Implementing test cases as programs to automate test execution is a popular testing practice. Current industrial practices usually use test functions to implement the test steps of a test case and then to compose the executable test case by choosing the test functions to call manually. It is time-co...

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Vydané v:IEEE transactions on reliability Ročník 71; číslo 2; s. 1127 - 1140
Hlavní autori: Liu, Kaiqi, Wu, Ji, Yang, Haiyan, Sun, Qing, Wan, Ruiyuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Application programming interface (API) recommendation
Bridges
deep structured semantic model (DSSM)
Documentation
Java
Libraries
Semantics
Similarity
Sun
Task analysis
test function recommendation
test function usage scenario
test scenario
ISSN:0018-9529, 1558-1721
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Shrnutí:Implementing test cases as programs to automate test execution is a popular testing practice. Current industrial practices usually use test functions to implement the test steps of a test case and then to compose the executable test case by choosing the test functions to call manually. It is time-consuming and could lead to invalid test results by selecting inappropriate test functions. In this article, we propose an automatic test function recommendation approach named Scenario-based Recommendation of TEst Function (SRTEF). Given a test step of a test case, SRTEF uses the weighted description similarity and the scenario similarity to recommend test functions. The description similarity utilizes the deep structured semantic model (DSSM) to measure the relatedness between a test step and a test function by their literal descriptions. The test scenario and the test function usage scenario are considered to calculate the scenario similarity. SRTEF has been successfully applied in Huawei. The systematic experiments have been conducted to evaluate SRTEF by using the dataset from Huawei and comparing with BiInformation source-based KnowledgE Recommendation (BIKER), reported as the best approach so far. The results show that SRTEF outperforms BIKER with significant positive ratios consistently in all the three selection strategies, i.e., Top-3, Top-5, and Top-10. The DSSM shows its advantage over word embedding by the double performance of capturing the semantic relatedness in SRTEF.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2022.3164645