# Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques
## 5 Results and Discussions
### Answers to the Research Questions
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Study No.** & **Authors and Title** & **Score** \\ \hline PS01 & [PERSON] et al. Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction 2015 [39] & 10.0 \\ \hline PS02 & [PERSON], [PERSON], Software Risk Estimation Through Bug Reports Analysis and Bug-fix Time Predictions & 10.0 \\ \hline PS03 & [PERSON], et al. A Dataset for Software Requirements Risk Prediction 2018 [7] & 9.5 \\ \hline PS04 & [PERSON], et al. Risk management in the software life cycle: A systematic literature review 2020 [41] & 9.5 \\ \hline PS05 & [PERSON], et al. Software project risk analysis using Bayesian networks with causality constraints 2013 [42] & 9.5 \\ \hline PS06 & [PERSON], [PERSON], Prediction of Risk Percentage in Software Projects by Training Machine Learning Classifiers 2021 [43] & 9.0 \\ \hline PS07 & [PERSON], et al. An integrative framework for intelligent software project risk planning 2013 [44] & 9.0 \\ \hline PS08 & [PERSON], et al. Software Risk Prediction Based on the Hybrid Algorithm of Genetic Algorithm and Decision Tree 2007 [45] & 9.0 \\ \hline PS09 & [PERSON], et al. Prediction of project problem effects on software risk factors 2013 [46] & 8.5 \\ \hline PS10 & [PERSON] et al. Prioritizing Software Components Risk: Towards a Machine Learning-based Approach 2020 [47] & 8.0 \\ \hline PS11 & [PERSON], [PERSON], An integrated approach using IF-TOPSIS, fuzzy DEMATEL, and enhanced CSA optimized ANFIS for software risk prediction 2021 [48] & 8.0 \\ \hline PS12 & [PERSON] et al. Research on Software Project Risk Assessment Model based on Fuzzy Theory and Improved 2017 [49] & 7.5 \\ \hline PS13 & [PERSON] et al. Design and Development of Machine Learning Technique for Software Project Risk Assessment— & 7.5 \\ \hline PS14 & [PERSON] et al. A Risk Assessment Method based on Software Behavior 2019 [51] & 7.0 \\ \hline PS15 & [PERSON], [PERSON], A Probabilistic Software Risk Assessment and Estimation Model for Software Projects & 7.0 \\ \hline PS16 & [PERSON], Risk Group Prediction of Software Projects Using Machine Learning Algorithm 2021 [52] & 7.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Scores of quality of PS under the categories ‘very high’ and ‘high’. RQ04: What is the size of the datasets? The purpose of examining the size of datasets is to determine the external validity of the research under discussion. When a large sample of a dataset is used rather than a small dataset, the external validity of the results is improved. In addition, the size of the dataset might have an impact on the results of detection models when used in conjunction with other techniques. When developed on large-scale training data, a detection model has a large learning area, which increases the likelihood of providing more positive outcomes. The studies were separated into three groups according to the size of the datasets that were analyzed. We were able to gather the relevant information about the sample size as well as the size of the data sets that were used in each of the 16 articles we reviewed. Our definition of a \"Large\" study was one that used more than 200 samples, a \"Medium\" study was one that used 100 to 200 samples, and a \"Small\" study was one that used zero to 100 samples. Another class has been defined as \"Unknown\" because the sample sizes of the dataset used in those studies were missing. Table 7 summarizes the number of samples in each of the datasets that were used, the number of studies that were conducted, and the percentage of studies that fell into each of the three categories that were conducted. RQ05: What data analysis approaches are utilized to develop SRPM models? In the primary studies we have selected, we found two major approaches for data analyses: (1) Machine Learning Approach and (2) Statistical approach. Although most of the papers conducted a machine learning approach. In Figure 4, the ratio of machine learning and statistical approaches is shown: