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ThinkMind // ICSEA 2020, The Fifteenth International Conference on Software Engineering Advances // View article icsea_2020_1_130_10058
Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases
 Authors:
Cristina Landin
Leo Hatvani
Sahar Tahvili
Hugo Haggren
Martin Längkvist
Amy Loutfi
Anne Håkansson Keywords: Natural Language Processing, Deep Learning, Software Testing, Semantic Analysis, Test Optimization Abstract:
Software testing is still heavily dependent on human judgment since a large portion of testing artifacts, such as requirements and test cases are written in a natural text by experts. Identifying and classifying relevant test cases in large test suites is a challenging and also time-consuming task. Moreover, to optimize the testing process test cases should be distinguished based on their properties, such as their dependencies and similarities. Knowing the mentioned properties at an early stage of the testing process can be utilized for several test optimization purposes, such as test case selection, prioritization, scheduling, and also parallel test execution. In this paper, we apply, evaluate, and compare the performance of two deep learning algorithms to detect the similarities between manual integration test cases. The feasibility of the mentioned algorithms is later examined in a Telecom domain by analyzing the test specifications of five different products in the product development unit at Ericsson AB in Sweden. The empirical evaluation indicates that utilizing deep learning algorithms for finding the similarities between manual integration test cases can lead to outstanding results. Pages: 90 to 97 Copyright: Copyright (c) IARIA, 2020 Publication date: October 18, 2020 Published in: conference ISSN: 2308-4235 ISBN: 978-1-61208-827-3 Location: Porto, Portugal Dates: from October 18, 2020 to October 22, 2020
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