An Artificial Immune System for Black Box Test Case Selection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 12692 LNCS, Page: 169-184
2021
- 4Citations
- 5Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Testing is a crucial part of the development of a new product. For software validation a transformation from manual to automated tests can be observed which enables companies to implement large numbers of test cases. However, during testing situations may occur where it is not feasible to run all tests due to time constraints. Hence a set of critical test cases must be compiled which usually fulfills several criteria. Within this work we focus on criteria that are feasible for black box testing such as system tests. We adapt an existing artificial immune system for our use case and evaluate our method in a series of experiments using industrial datasets. We compare our approach with several other test selection methods where our algorithm shows superior performance.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85107383052&origin=inward; http://dx.doi.org/10.1007/978-3-030-72904-2_11; http://link.springer.com/10.1007/978-3-030-72904-2_11; http://link.springer.com/content/pdf/10.1007/978-3-030-72904-2_11; https://dx.doi.org/10.1007/978-3-030-72904-2_11; https://link.springer.com/chapter/10.1007/978-3-030-72904-2_11
Springer Science and Business Media LLC
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