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Automated Functional Dependency Detection Between Test Cases Using Text Semantic Similarity
RISE SICS Västerås.ORCID iD: 0000-0002-8724-9049
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-0073-1674
University of Innsbruck Innsbruck, Austria.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-0611-2655
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2019 (English)In: 2019 IEEE International Conference On Artificial Intelligence Testing (AITest), 2019, p. 19-26, article id 8718215Conference paper, Published paper (Other academic)
Abstract [en]

Knowing about dependencies and similarities between test cases is beneficial for prioritizing them for cost-effective test execution. This holds especially true for the time consuming, manual execution of integration test cases written in natural language. Test case dependencies are typically derived from requirements and design artifacts. However, such artifacts are not always available, and the derivation process can be very time-consuming. In this paper, we propose, apply and evaluate a novel approach that derives test cases' similarities and functional dependencies directly from the test specification documents written in natural language, without requiring any other data source. Our approach uses an implementation of Doc2Vec algorithm to detect text-semantic similarities between test cases and then groups them using two clustering algorithms HDBSCAN and FCM. The correlation between test case text-semantic similarities and their functional dependencies is evaluated in the context of an on-board train control system from Bombardier Transportation AB in Sweden. For this system, the dependencies between the test cases were previously derived and are compared to the results our approach. The results show that of the two evaluated clustering algorithms, HDBSCAN has better performance than FCM or a dummy classifier. The classification methods' results are of reasonable quality and especially useful from an industrial point of view. Finally, performing a random undersampling approach to correct the imbalanced data distribution results in an F1 Score of up to 75% when applying the HDBSCAN clustering algorithm.

Place, publisher, year, edition, pages
2019. p. 19-26, article id 8718215
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:mdh:diva-41272DOI: 10.1109/AITest.2019.00-13ISI: 000470916100004Scopus ID: 2-s2.0-85067096441ISBN: 9781728104928 (print)OAI: oai:DiVA.org:mdh-41272DiVA, id: diva2:1260293
Conference
2019 IEEE International Conference On Artificial Intelligence Testing (AITest), 4-9 April 2019, Newark, CA, USA
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-06-27Bibliographically approved
In thesis
1. Multi-Criteria Optimization of System Integration Testing
Open this publication in new window or tab >>Multi-Criteria Optimization of System Integration Testing
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Optimizing software testing process has received much attention over the last few decades. Test optimization is typically seen as a multi-criteria decision making problem. One aspect of test optimization involves test selection, prioritization and execution scheduling. Having an efficient test process can result in the satisfaction of many objectives such as cost and time minimization. It can also lead to on-time delivery and a better quality of the final software product. To achieve the goal of test efficiency, a set of criteria, having an impact on the test cases, need to be identified. The analysis of several industrial case studies and also state of the art in this thesis, indicate that the dependency between integration test cases is one such criterion, with a direct impact on the test execution results. Other criteria of interest include requirement coverage and test execution time. In this doctoral thesis, we introduce, apply and evaluate a set of approaches and tools for test execution optimization at industrial integration testing level in embedded software development. Furthermore, ESPRET (Estimation and Prediction of Execution Time) and sOrTES (Stochastic Optimizing of Test Case Scheduling) are our proposed supportive tools for predicting the execution time and the scheduling of manual integration test cases, respectively. All proposed methods and tools in this thesis, have been evaluated at industrial testing projects at Bombardier Transportation (BT) in Sweden. As a result of the scientific contributions made in this doctoral thesis, employing the proposed approaches has led to an improvement in terms of reducing redundant test execution failures of up to 40% with respect to the current test execution approach at BT. Moreover, an increase in the requirements coverage of up to 9.6% is observed at BT. In summary, the application of the proposed approaches in this doctoral thesis has shown to give considerable gains by optimizing test schedules in system integration testing of embedded software development.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2018
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 281
National Category
Embedded Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-41273 (URN)978-91-7485-414-5 (ISBN)
Public defence
2018-12-21, Lambda, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2018-11-02 Created: 2018-11-01 Last updated: 2018-11-20Bibliographically approved

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Tahvili, SaharHatvani, LeoAfzal, WasifBohlin, Markus

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