Description
Big Data is a crucial pillar for many of today’s newly emerging business models. Areas of application range from consumer analysis over medicine to fraud detection. All of those domains require reliable software. Even though imperfect results are accepted in Big Data software, bugs and other defects can have drastic consequences. Therefore, in this paper, the software engineering sub discipline of testing is addressed. Big Data exhibits characteristics which differentiate its processing software from those that process traditional workloads. Consequently, an architecture pattern for testing that can be integrated into development environments for Big Data software is proposed. The paper features a detailed description of the artifact as well as a preliminary plan for evaluation.
Testing in Big Data: An Architecture Pattern for a Development Environment for Innovative, Integrated and Robust Applications
Big Data is a crucial pillar for many of today’s newly emerging business models. Areas of application range from consumer analysis over medicine to fraud detection. All of those domains require reliable software. Even though imperfect results are accepted in Big Data software, bugs and other defects can have drastic consequences. Therefore, in this paper, the software engineering sub discipline of testing is addressed. Big Data exhibits characteristics which differentiate its processing software from those that process traditional workloads. Consequently, an architecture pattern for testing that can be integrated into development environments for Big Data software is proposed. The paper features a detailed description of the artifact as well as a preliminary plan for evaluation.