Use Case: Optimization of Regression Tests—Reduction of the Test Portfolio Through Representative Identification

Volker Liermann (), Sangmeng Li () and Christoph Wünnemann ()
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Volker Liermann: ifb SE
Sangmeng Li: ifb SE
Christoph Wünnemann: ifb SE

A chapter in The Digital Journey of Banking and Insurance, Volume II, 2021, pp 5-19 from Springer

Abstract: Abstract Regression tests are widely used after release updates to ensure the software and implementation stability. The brute force approach is to recalculate the whole portfolio during this regression test with an enormous burden on disk space, computational power, and workforce for the error tracking. The authors provide specialized cluster algorithms to significantly reduce the number of tested transactions to a few representative transactions, by maintaining the overall software and implementation stability on the same level.

Keywords: Regression test; Cluster analysis; Machine learning; Deep learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-78829-2_1

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DOI: 10.1007/978-3-030-78829-2_1

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