A Predictive Model for Software Cost Estimation Using ARIMA Algorithm

Technology is a differentiator in business today. It plays a different and decisive role by providing programs that contribute to this. To build this software while avoiding risks during the implementation and construction process, it is necessary to estimate the cost. The cost estimation process is the process of estimating the effort, time, and resources needed to build a software project. It is a crucial process as it provides good planning during the construction and implementation process and reduces the risks you may be exposed to. Therefore, previous studies sought to build models and methods to estimate this, but they were not accurate enough to complete the process. Therefore, this study seeks to build a model using the Autoregressive integrated moving average (ARIMA) algorithm. Five datasets the COCOMO81, COCOMONasaV1, COCOMONasaV2, Desharnais, and China were used. The dataset was processed to remove noise and missing values, visualized to understand it, and linked using a time series to predict the future values of the data. It will then be trained on the ARIMA algorithm. To ensure the effectiveness and efficiency of the model for use, four famous evaluation criteria were used: mean magnitude of relative error (MMRE), root mean square error (RMSE), mean magnitude of relative error (MdMRE), and prediction accuracy (PRED). This experiment showed impressive software cost estimation results, with MMRE, RMSE, MdMRE, and PRED results being 0.07613, 0.04999, 0.03813, and 95% for the COCOMO81 dataset, respectively. The results were high for the COCOMONasaV1 dataset, reaching 0.02227, 0.02899, 0.01113, and 97.1%. The COCOMONasaV2 results were 0.01035, 0.00650, 0.00517, and 99.35%, respectively. The China dataset showed good prediction results of 0.00001, 0.00430, 0.00008, and 99.57%, respectively. The results were impressive and promising for the Desharnais dataset, showing 0.00004, 0.0039, 0.00002, and 99.6%. The results of this study are promising and distinctive compared to recent studies, and they also contribute to good business planning and risk reduction.