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Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases


Fulltext:


Authors:

Cristina Landin, Leo Hatvani, Sahar Tahvili, Hugo Haggren , Martin Längkvist , Amy Loutfi , Anne Håkansson

Research group:

Software Testing Laboratory

Publication Type:

Conference/Workshop Paper

Venue:

The Fifteenth International Conference on Software Engineering Advances

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 people. 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.

Bibtex

@inproceedings{Landin5878,
author = {Cristina Landin and Leo Hatvani and Sahar Tahvili and Hugo Haggren and Martin L{\"a}ngkvist and Amy Loutfi and Anne H{\aa}kansson},
title = {Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases},
month = {October},
year = {2020},
booktitle = {The Fifteenth International Conference on Software Engineering Advances},
url = {http://www.es.mdu.se/publications/5878-}
}