You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

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

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.mdh.se/publications/5878-}
}