# Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques
## 5 Results and Discussions
### Description of Primary Studies (PS)  
An SLR inquiry in the realm of SRPM is new to us to the extent that we are aware. The 16 PS were rated according to a variety of selection criteria. Each study has its unique identification and reference number, which are listed in Table 3.  
Partially oriented studies were discarded in favor of the 16 articles devoted exclusively to SRPM research. Primary studies detection models are described in the following lines, including study methods, findings, and their effectiveness:  
[PERSON] and [PERSON] [18]'s Bayesian Belief Network (BBN)-based probabilistic software risk estimate model, which focuses on the most significant software risk indicators, was developed to be used for risk assessment in software development projects. An empirical experiment was carried out to evaluate the model that had been built, using data obtained from software development projects that were used by the business. \begin{table}
\begin{tabular}{l l} \hline
**QQ\#** & **Quality Assessment Questions** \\ \hline QQ01 & Is it apparent what the study’s main objective is? \\ \hline QQ02 & Is the sample of the dataset large enough to support this sort of investigation? \\ \hline QQ03 & Whether the procedure for data collection is adequately specified and documented? \\ \hline QQ04 & Is there enough information on the experiment provided by the author? \\ \hline QQ05 & Are the potential risks to the validity of the claim described? \\ \hline QQ06 & Are the study’s limitations discussed in detail? \\ \hline QQ07 & Is there a clear definition of the performance parameters employed in the study? \\ \hline QQ08 & How well-defined are the learning strategies? \\ \hline QQ09 & Are the results presented in a clear and concise manner? \\ \hline QQ10 & Is there a way to compare and contrast different techniques? \\ \hline QQ11 & Are there any new insights from this study that can be added to the existing literature? \\ \hline \end{tabular}
\end{table} 
Table 2: Questions for Quality Analysis. [PERSON] et al. [39] reviewed similar work from the previous two decades and discovered that all available models for prediction assume equal misclassification costs, by neglecting the effects of real-world events in the software project management industry. Indeed, failing to recognize project failure is even more dangerous than incorrectly labeling a project with a high possibility of success as a failure, which is much more common. Furthermore, ensemble learning, which is a well-established technique for improving prediction performance in other areas, has not been substantially examined in the context of software project risk prediction. Their research aimed to fill knowledge gaps in the field by investigating cost-sensitive analysis and classifier ensemble approaches, among other things, both of which were investigated. Using 327 outsourced software project examples, a T-test comparison of 60 alternative risk prediction models revealed that the optimal model is a homogenized set of decision trees (DT) adopting bagging. The findings of the proposed framework reveal that while DT beat Support Vector Machine (SVM) in terms of accuracy (i.e., assuming equal misclassification costs), it outperformed SVM in terms of cost-sensitive analysis. For a quick overview, this paper proposes the first cost-sensitive and ensemble-based hybrid modeling approach for predicting the risk associated with software development projects. A cost-of-misclassification evaluation criterion was also created to evaluate software risk and prediction models. According to [PERSON] et al. [44], software project risk assessment and planning have no empirical models. The researchers developed an integrated framework for intelligent software project risk planning to help reduce project risks and increase predictability (IF