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Software Effort Estimating (SEE) is a fundamental task in all software development lifecycles and procedures. Therefore, when deciding how to anticipate effort in a variety of project types, the comparative assessment of effort prediction methods has emerged as a standard strategy. Unfortunately, these studies include a range of sample techniques and error metrics, making a comparison with other work challenging. To overcome these drawbacks, this study proposes a deep learning model to effectively estimate software effort. The estimation is mainly focused on minimizing the cost and time consumption. The input data is taken from the dataset and preprocessing is performed to remove the noise content. Then the required features are extracted using the preprocessed data with the help of the simple and higher-order statistical features. A novel Modified Chaotic Enriched Jaya with Moth Flame Optimization (MCEJMO) algorithm is introduced for feature selection to enhance SEE accuracy. The estimation is performed using Multilayer Long Short-Term Memory (M-LSTM). The proposed method achieved a Mean Square Error (MSE) of 0.2825 for dataset 1 and 0.2285 for dataset 2.
Bbadana et al. (Mon,) studied this question.