Study of algorithmic method and model for effort estimation in big data software development case study: Geodatabase

Abstract

During the early phase of software development, there are works to be done. One of them is conducting an effort estimation. Without a proper process, the development may find itself overlook the targeted budget. This paper focus on the study of effort estimation in Big Data software development. Big Data definitions and characteristics may altered the current method and model that used to estimate the effort. As author undergo the study, it shows that one of the most appropriate method to be used is the algorithmic method. Thus, the study is concentrated in the use of algorithmic method and three models within it, namely the COCOMO II, Function Point Analysis, and Use Case-based Estimation. The study result is tested using a study case of Geodatabase. Geodatabase is a software which process Big Data and developed by the Center of Data and Analysis of Rural Development (Pusat Data dan Analisa Pembangunan — Badan Perencanaan dan Pengembangan Daerah Provinsi Jawa Barat). The study concludes that the practice of using algorithmic method and models is able to deliver an estimation value. However, the value is being convergent only on particular models and their variations. Thus it can be concluded that it is better to combine various methods and models in order to achieve better accuracy in effort estimation.