Authors:
Christian Daase
;
Daniel Staegemann
and
Klaus Turowski
Affiliation:
Institute of Technical and Business Information Systems, Otto-von-Guericke University, Magdeburg, Germany
Keyword(s):
Big Data Analytics, Software Testing, Quality Assurance, Systematic Literature Review.
Abstract:
As the complexity and diversity of big data systems reaches a new level, testing the solutions developed is becoming increasingly difficult. In this study, a systematic literature review is conducted on the role of testing and related quality assurance techniques in current big data systems in terms of applied strategies and design guidelines. After briefly introducing the necessary knowledge about big data in general, the methodology is explained in a detailed and reproducible manner, including the reasoned division of the main question into two concise research questions. The results show that methods such as individual experiments, standardized benchmarking, case studies and preparatory surveys are among the preferred approaches, but also have some drawbacks that need to be considered. In conclusion, testing alone may not guarantee a perfectly operating system, but can serve to minimize malfunctions to a limited number of special cases by revealing its principal weaknesses.