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Cluster-Based Parallel Testing Using Semantic Analysis
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-3054-0051
Global Artificial Intelligence Accelerator (GAIA), Ericsson AB, Stockholm, Sweden; School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
Global Artificial Intelligence Accelerator (GAIA), Ericsson AB, Stockholm, Sweden.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-0579-7181
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2020 (English)In: 2020 IEEE International Conference On Artificial Intelligence Testing (AITest), IEEE, 2020, p. 99-106Conference paper, Published paper (Refereed)
Abstract [en]

Finding a balance between testing goals and testing resources can be considered as a most challenging issue, therefore test optimization plays a vital role in the area of software testing. Several parameters such as the objectives of the tests, test cases similarities and dependencies between test cases need to be considered, before attempting any optimization approach. However, analyzing corresponding testing artifacts (e.g. requirement specification, test cases) for capturing the mentioned parameters is a complicated task especially in a manual testing procedure, where the test cases are documented as a natural text written by a human. Thus, utilizing artificial intelligence techniques in the process of analyzing complex and sometimes ambiguous test data, is considered to be working in different industries. Test scheduling is one of the most popular and practical ways to optimize the testing process. Having a group of test cases which are required the same system setup, installation or testing the same functionality can lead to a more efficient testing process. In this paper, we propose, apply and evaluate a natural language processing-based approach that derives test cases' similarities directly from their test specification. The proposed approach utilizes the Levenshtein distance and converts each test case into a string. Test cases are then grouped into several clusters based on their similarities. Finally, a set of cluster-based parallel test scheduling strategies are proposed for execution. The feasibility of the proposed approach is studied by an empirical evaluation that has been performed on a Telecom use-case at Ericsson in Sweden and indicates promising results.

Place, publisher, year, edition, pages
IEEE, 2020. p. 99-106
Series
IEEE International Conference on Artificial Intelligence Testing (AITest)
Keywords [en]
Software Testing, Natural Language Processing, Test Optimization, Semantic Similarity, Clustering
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-88654DOI: 10.1109/AITEST49225.2020.00022ISI: 000583824000015Scopus ID: 2-s2.0-85092313008ISBN: 978-1-7281-6984-2 (print)OAI: oai:DiVA.org:oru-88654DiVA, id: diva2:1519695
Conference
2nd IEEE International Conference on Artificial Intelligence Testing (AITest 2020), Oxford, United Kingdom, August 3-6, 2020
Funder
Knowledge FoundationVinnovaAvailable from: 2021-01-19 Created: 2021-01-19 Last updated: 2023-10-05Bibliographically approved
In thesis
1. AI-Based Methods For Improved Testing of Radio Base Stations: A Case Study Towards Intelligent Manufacturing
Open this publication in new window or tab >>AI-Based Methods For Improved Testing of Radio Base Stations: A Case Study Towards Intelligent Manufacturing
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Testing of complex systems may often require the use of tailored-made solutions, expensive testing equipment, large computing capacity, and manual implementation work due to domain uniqueness. The aforementioned test resources are expensive and time-consuming, which makes them good candidates to optimize. A radio base station (RBS) is a complex system. Upon the arrival of new RBS generations, new testing challenges have been introduced that traditional methods cannot cope with. In order to optimize the test process of RBSs, product quality and production efficiency can be studied.

Despite that AI techniques are valuable tools for monitoring behavioral changes in various applications, there have not been sufficient research efforts spent on the use of intelligent manufacturing in already existing factories and production lines. The concept of intelligent manufacturing involves the whole system development life-cycle, such as design, production, and maintenance. Available literature about optimization and integration of industrial applications using AI techniques has not resulted in common solutions due to the complexity of the real-world applications, which have their own unique characteristics, e.g., multivariate, non-linear, non-stationary, multi-modal, class imbalance; making it challenging to find generalizable solutions. This licentiate thesis aims to bridge the gap between theoretical approaches and the implementation of real industrial applications. 

In this licentiate thesis, two questions are explored, namely how well AI techniques can perform and optimize fault detection and fault prediction on the production of RBSs, as well as how to modify learning algorithms in order to perform transfer learning between different products. These questions are addressed by using different AI techniques for test optimization purposes and are examined in three empirical studies focused on parallel test execution, fault detection and prediction, and automated fault localization. For the parallel test execution study, two different approaches were used to find and cluster semantically similar test cases and propose their execution in parallel. For this purpose, Levenshstein distance and two NLP techniques are compared. The results show that cluster-based test scenarios can be automatically generated from requirement specifications and the execution of semantically similar tests can reduce the number of tests by 95\% in the study case if executed in parallel. 

Study number two investigates the possibility of predicting testing performance outcomes by analyzing anomalies in the test process and classifying them by their compliance with dynamic test limits instead of fixed limits. The performance measures can be modeled using historical data through regression techniques and the classification of the anomalies is learned using support vector machines and convolutional neural networks. The results show good agreement between the actual and predicted learned model, where the root-mean-square error reaches 0.00073. Furthermore, this approach can automatically label the incoming tests according to the dynamic limits, making it possible to predict errors in an early stage of the process. This study contributes to product quality by monitoring the test measurements beyond fixed limits and contributes to making a more efficient testing process by detecting faults before they are measured. Moreover, study two considers the possibility of using transfer learning due to an insufficient number of anomalies in a single product. 

The last study focuses on root cause analysis by analyzing test dependencies between test measurements using two known correlation-based methods and mutual information to find strength associations between measurements. The contributions of this study are twofold. First, test dependencies between measurements can be found using Pearson and Spearman correlation and MI; and their dependencies can be linear or higher order. Second, by clustering the associated tests, redundant tests are found, which could be used to update the test execution sequence and choose to execute only the relevant tests, hence, making a more efficient production process by saving test time.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2023. p. 34
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 102
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-108714 (URN)
Presentation
2023-10-02, Örebro universitet, Prismahuset, Hörsal P1, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 20190128Vinnova, D-RODS (2023-00244)
Available from: 2023-10-05 Created: 2023-10-03 Last updated: 2023-10-05Bibliographically approved

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Landin, CristinaLängkvist, MartinLoutfi, Amy

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