Geliştirilmiş AI tekniğini kullanan yazılım hatası tahmini için yeni çerçeve

In this thesis, new framework planned for software defect estimation using LSTM using TSA. The proposed framework combined AI technique with TSA to optimize the defect predication accuracy. Then, in the first stage the software defect dataset become input to the PCA. Furthermore, the TSA applied to optimize the bias and weight of the LSTM. The proposed framework validated by using number of datasets proposed by NASA datasets to improve the framework results. The experimental results presented by using by calculating several parameters to evaluate the proposed method. Additionally, the obtained results compared with various studies proposed in this field.