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Investigating the use of duration‐based windows and estimation by analogy for COCOMO
Author(s) -
Nguyen Vu,
Huynh Thuy,
Boehm Barry,
Huang LiGuo,
Truong Thong
Publication year - 2019
Publication title -
journal of software: evolution and process
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 29
eISSN - 2047-7481
pISSN - 2047-7473
DOI - 10.1002/smr.2176
Subject(s) - cocomo , analogy , estimation , computer science , duration (music) , data mining , software , set (abstract data type) , data set , machine learning , artificial intelligence , software development , engineering , systems engineering , art , software construction , linguistics , philosophy , literature , programming language
In model‐based software estimation, using the right training data is a key contributor for making accurate predictions, which is crucial for the success of software projects. This study investigates the use of duration‐based windows and estimation by analogy to calibrate COCOMO and assess their estimation performance. We compare these approaches as well as the use of all available historical data using the COCOMO data set of 341 projects and NASA data set of 93 projects. The results show that timing information exists in the data sets affecting estimation accuracy. Given sufficient data for calibration, using recently completed projects within short durations generates more accurate estimates than retaining all historical data or using k ‐nearest neighbors based on estimation by analogy. More training data spanning a long period of time may not lead to improved estimation accuracy. This study offers evidence to support the use of projects completed within recent years for training estimation models.