Applying Combinatorial Testing to Large-scale Data Processing at Adobe

Published

April 22, 2019

Author(s)

Raghu N. Kacker, David R. Kuhn, Riley Smith

Abstract

Adobe offers an analytics product as part of the Marketing Cloud software with which customers can track many details about users across various digital platforms. For the most part, customers define the amount and type of data to track. This high dimensionality makes validation difficult or intractable. Due to increasing attention from both industry and academia, combinatorial testing was investigated and applied to improve existing validation. In this paper, we report the practical application of combinatorial testing to the data collection, compression and processing components of the Adobe analytics product. Consequently, the effectiveness of combinatorial testing for this application is measured in terms of new defects found rather than detecting known defects from previous versions. The results of the application show that combinatorial testing is an effective way to improve validation for these components of Adobe Analytics. In addition, we report the details of the input parameter modeling process and test value selection to provide more context for the problem and how combinatorial testing provides the structure to improve validation for Adobe Analytics.

Proceedings Title

Proceedings of IEEE International Conference on Software Testing, Verification and Validation
ICST 2019 workshops

Conference Dates

April 22-27, 2019

Conference Location

Xian

Conference Title

IEEE International Conference on Software Testing, Verification and Validation
ICST 2019 workshops

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/ICSTW.2019.00051

Local Download

Keywords

Combinatorial Testing, Industry, Application

Citation

Kacker, R. , Kuhn, D. and Smith, R. (2019), Applying Combinatorial Testing to Large-scale Data Processing at Adobe, Proceedings of IEEE International Conference on Software Testing, Verification and Validation ICST 2019 workshops, Xian, -1, [online], https://doi.org/10.1109/ICSTW.2019.00051 (Accessed April 26, 2024)