Big Data for Software Engineering Estimation: Pros and Cons
by Srinivasa Gopal
Big data describes the large, complex datasets that are collected and stored by organizations. This data can improve many aspects of software engineering, including estimation.
One of the main benefits of using big data in software engineering estimation is that it can help to improve the accuracy of estimates. This is because big data can be used to identify patterns and trends that can be used to predict the effort required to develop a software project. For example, big data can be used to track the time it takes to develop similar software projects in the past. This information can then be used to estimate the effort required for a new project.
Another benefit of using big data in software engineering estimation is that it can help to identify risks. This is because big data can be used to identify factors that can affect the effort required to develop a software project. For example, big data can be used to track the impact of changes in the requirements or the complexity of the software. This information can then be used to identify risks and to develop mitigation strategies.
However, there are also some challenges associated with using big data in software engineering estimation. One challenge is that big data can be difficult to collect and store. This is because big data can be generated from a variety of sources, and it can be in a variety of formats. Another challenge is that big data can be difficult to analyze. This is because big data can be very large and complex. Despite these challenges, big data has the potential to significantly improve the accuracy and efficiency of software engineering estimation. As big data becomes more accessible and affordable, it is likely that its use in software engineering estimation will become more widespread.
Here are some of the specific pros and cons of using big data in software engineering estimation:
Pros:
- Improved accuracy of estimates
- Identification of risks
- Increased efficiency
- Better decision-making
- Increased transparency
- Improved communication
Cons:
- High cost of collecting and storing big data
- Difficulty in analyzing big data
- Lack of expertise in big data analytics
- Legal and regulatory challenges
- Security and privacy concerns
Overall, the use of big data in software engineering estimation has the potential to be a valuable tool for improving the accuracy, efficiency, and transparency of the estimation process. However, it is important to be aware of the challenges associated with using big data before adopting this approach.
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About Srinivasa Gopal
Srinivasa Gopal obtained a Mechanical Engineering degree from the Indian Institute of Technology, Chennai, in the year 1990, a MS by Research degree in Industrial Systems Engineering in 1992 from the University of Regina in Saskatchewan, Canada, and a MS by Research degree in Information Technology in 2013 from the International Institute of Information Technology, Bengaluru. He was also awarded the Governor General of Canada’s Academic Medal (Gold) in 1992. Srinivasa Gopal has worked for leading multinational companies such as Infosys consultants, Emirates Airlines, Unisys Corporation, LandMark Gulf Group, and GAVS Info Services. He has published in the India Software Engineering Conference 2012, the International Conference on Computers and Industrial Engineering 1991, Orlando, Florida, and the International Journal of Advanced Manufacturing Technology 1996. He is one of the co-founders of the Ramanujan Society for Academic Research and Promotion of Science, a non-profit society for academic research and science.
