Published Paper Details:

A method for guaranteeing the high standard of software as a whole is quality assurance. Software products utilised for data analytics are distinct from more conventional ones, requiring a particular set of skills and more time for quality assurance. The development and implementation of several big data application systems across different fields have been prompted by the rapid expansion of the big data technology industry, which is now seeing a compound annual growth rate of 27%. The 4Vs of big data are volume, velocity, variety, and veracity, and these systems use a wide array of platforms, tools, and procedures to gather, process, analyse, and forecast this data. However, ensuring the quality of these systems poses significant challenges due to a complexity and scale of a data involved. This paper offers a thorough overview of quality assurance (QA) concepts and components for big data systems, focussing on important criteria including timeliness, consistency, credibility, and accessibility. Validation techniques such as classification-based testing, program-based testing, and metamorphic testing are presented, and the article goes on to talk about the special QA problems caused by the varied and ever-changing nature of big data. Through a systematic literature review, this study synthesises existing QA technologies and their strengths and limitations, offering insights into the current state of QA in big data applications and identifying areas for future research and improvement.