Cluster-Based Parallel Testing Using Semantic AnalysisShow others and affiliations
2020 (English)In: 2020 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE TESTING (AITEST), IEEE COMPUTER SOC , 2020, p. 99-106, article id 162653Conference 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 COMPUTER SOC , 2020. p. 99-106, article id 162653
Keywords [en]
Software Testing, Natural Language Processing, Test Optimization, Semantic Similarity, Clustering
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-52956DOI: 10.1109/AITEST49225.2020.00022ISI: 000583824000015Scopus ID: 2-s2.0-85092313008OAI: oai:DiVA.org:mdh-52956DiVA, id: diva2:1514908
Conference
2nd IEEE International Conference on Artificial Intelligence Testing, AITest 2020
2021-01-072021-01-072021-11-05Bibliographically approved