Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

TL;DR

This paper provides a comprehensive review of Big Data testing techniques, challenges, and future directions, highlighting the predominance of MapReduce validation challenges and the use of combinatorial testing methods.

Contribution

It systematically reviews Big Data testing techniques from 2010 to 2021, identifying key challenges and the prevalent use of combinatorial testing in the field.

Findings

Diverse testing techniques address specific Big Data problems.

Most testing challenges occur during MapReduce validation.

Combinatorial testing is widely used with other techniques.

Abstract

Big Data is reforming many industrial domains by providing decision support through analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and quality of data. However, because of the diversity and complexity of data, testing Big Data is challenging. Though numerous research efforts deal with Big Data testing, a comprehensive review to address testing techniques and challenges of Big Data is not available as yet. Therefore, we have systematically reviewed the Big Data testing techniques evidence occurring in the period 2010-2021. This paper discusses testing data processing by highlighting the techniques used in every processing phase. Furthermore, we discuss the challenges and future directions. Our findings show that diverse functional, non-functional and combined (functional and non-functional) testing techniques have been used to solve specific problems related to Big Data. At the same time, most of the testing challenges have been faced during the MapReduce validation phase. In addition, the combinatorial testing technique is one of the most applied techniques in combination with other techniques (i.e., random testing, mutation testing, input space partitioning and equivalence testing) to find various functional faults through Big Data testing.