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| Abstract: |
| Effort estimation plays a crucial role in software development projects, aiding in resource allocation, project planning, and risk management. Traditional estimation techniques often struggle to provide accurate estimates due to the complex nature of software projects. In recent years, machine learning approaches have shown promise in improving the accuracy of effort estimation models. This study proposes a hybrid model that combines Long Short-Term Memory (LSTM) and Random Forest (RF) algorithms to enhance software effort estimation. The proposed hybrid model takes advantage of the strengths of both LSTM and Random Forest algorithms. To evaluate the performance of the hybrid model, an extensive set of software development projects is used as the experimental dataset. The experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and reliability. The integration of LSTM and Random Forest enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development data. The hybrid model enhances estimation accuracy, enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects. |
| Key words: software effort estimation hybrid model ensemble learning Long Short-Term Memory (LSTM) temporal dependencies non-linear relationships |
| DOI：10.11916/j.issn.1005-9113.2023082 |
| Clc Number:TP311 |
| Fund: |
