Abstract
Quality Assurance is a technique for ensuring the overall software quality suggested by Global Standards bodies like IEEE. The Quality Assurance for Data Analytics requires more time and a very different set of skills because Software Products, which are used for Data Analytics, are different than that of traditional ones. In result, these Software Products require more complex algorithms to operate and then for ensuring their quality, one needs more advanced techniques for handling these Software Products. According to our survey, Data Analytical Software Products require more work because of their more complex nature. One of the possible reasons can be the volume and variety of Data. On the same hand, this research emphasizes on testing of Data Analytical Software Products which have many issues because testing of these Software Products requires real data. However, every time the testing of these Software Products is based either on dummy data or simulations and these Software Products fail when they work in real time. For making these Software Products work well before and after deployment, we have to define certain Quality standards. In this way, we can get better result producing analytics Software Products for better results.
Highlights
Quality Assurance (QA) related activities are proven to be beneficial because it gives a confidence about the completion of requirements and needs that a user expect from a system, it can be the quality of product or it may be the accessibility and reliability, accessibility and reliability are just two qualities of systems, there are many more suggested by different experts
Majority of the people that is around 4/5th of the total sample has accepted that complexities in data analytical system are more than the traditional softwares. These results show that most of the Practitioners have clear understanding of data analytical softwares or at least they have a knowhow of these softwares
Quality Assurance for a software product is an essential part of development cycle and when it comes to Data Analytical Software, responsibility of Quality Assurance team increases due to the complexities because of such a huge amount of data and its variety
Summary
Quality Assurance (QA) related activities are proven to be beneficial because it gives a confidence about the completion of requirements and needs that a user expect from a system, it can be the quality of product or it may be the accessibility and reliability, accessibility and reliability are just two qualities of systems, there are many more suggested by different experts. We have seen tremendous change in data related work and as a result the complexities and quality issues in software products have grown since few decades, these gaps in Software development must be handled with the help of performing activities to perform software quality assurance. There is a need to define certain measures and approaches that must be followed to tackle quality issues of data driven softwares or data analytical softwares [4], [5]. In the field of health where data is everything and to handle and manage this huge amount of data we need some special software that is totally different from traditional softwares. To get some results from that data and its storage is not an easy task that’s why we need some special techniques and standards that can tackle this issue [12]
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