# Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques
## 5 Results and Discussions
### Answers to the Research Questions
The difference between the real value and the expected value for an example is the prediction error [55]. **Root Mean Squared Error (RMSE):** The standard deviation of the errors that occur while making a prediction on a dataset is known as the Root Mean Squared Error (RMSE). This is similar to MSE, except that the root of the number is taken into account when calculating the model's accuracy. **Matthews Correlation Coefficient (MCC):** The Matthews correlation coefficient (MCC) is a metric that indicates how closely true classes and projected instances are related [56]. It can be expressed as  
Figure 3:1  
**Kappa Statistic:** As the Kappa statistic takes the chance factor into account, it is essential to consider the outcomes using this method. If the kappa statistic is near to one, the classification without change factor was successful. **Median Absolute Error (MedAE):** The median absolute error is not affected by outliers. The loss is derived by averaging all of the absolute deviations between the true value and the prediction. It can be expressed as:  
Figure 3:2  
where,  
\(E=\) true value,  
\(E^{\prime}=\) predicted value,  
\(n=\) total number of instances. **R2:** This measure indicates how well a model matches a certain dataset. It shows how close the regression line is to the actual data values. The R2 value ranges from 0 to 1, with 0 indicating that the model does not match the given data and 1 indicating that the model fits the dataset correctly. Table 8 shows the performance metrics that were used in the 16 papers chosen. We could discover that five performance metrics, Accuracy, Precision, Recall, F-Measure, and Mean Absolute Error (MAE), are the most often-used measures. Among them, Accuracy is the most used among the studies. Moreover, Precision, Recall, F-Measure, and MCC are also very commonly used when datasets are unbalanced. Therefore, considering the type of datasets, one can determine which performance measures should be chosen for the prediction models. RQ09: What is the efficiency of the SRPM that has been proposed? In this part, we summarize the performance of the PS of SRPM in this part. We looked at the results of all 16 PS to get an answer to this question. Allowing for any rating of studies on the basis of performance is highly difficult. We discovered that they use a variety of performance indicators, making it impossible to compare their results. In Table 9, the value of the most considered and five performance metrics (Accuracy, Precision, Recall, F-Measure, and MAE) employed in the investigations was shown. For each performance metric, the highest performer's values are highlighted. RQ10: What is the main research emphasis of the papers? The authors took various approaches in the primary studies, but the main research emphasis of the articles was to predict or assess software risk. Machine learning, statistical methods, and complex systems were all employed in the publications that were considered. Different kinds of classification and regression algorithms were employed in the machine learning approach. Inference and HMM techniques were employed for the statistical procedure. The Fuzzy Dematel approach was utilized for the complex system. \begin{table}
\begin{tabular}{l l} \hline \hline
**Performance Metrics** & **Primary Studies** \\ \hline Correctly Classified Instances & PS03, PS09 \\ \hline Incorrectly Classified Instances & PS03 \\ \hline Accuracy & PS01, PS05, PS06, PS07, PS08, PS10, PS12, PS14, PS16 \\ \hline Precision & PS07, PS09 \\ \hline Recall & PS07, PS09 \\ \hline F-Measure & PS07, PS09, PS10 \\ \hline Receiver Operating Characteristic (ROC) & PS10 \\ \hline Mean Absolute Error (MAE) & PS02, PS03 \\ \hline Mean Squared Error (MSE) & PS02 \\ \hline Root Mean Squared Error (RMSE) & PS03 \\ \hline Matthews Correlation Coefficient (MCC) & PS10 \\ \hline Kappa Statistic & PS09 \\ \hline Median Absolute Error (MedAE) & PS02 \\ \hline R\({}^{2}\) & PS02 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Employed performance metrics.