# Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques
## 5 Results and Discussions
### Description of Primary Studies (PS)
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt}} \hline \hline
**Study No.** & **Authors and Title** & **Ref No.** \\ \hline PS01 & Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction—[PERSON] et al. on 2015 & [39] \\ \hline PS02 & Software Risk Estimation Through Bug Reports Analysis and Bug-fix Time Predictions—[PERSON], [PERSON] on 2020 & [40] \\ \hline PS03 & A Dataset for Software Requirements Risk Prediction—[PERSON] et al. on 2018 & [7] \\ \hline PS04 & Risk management in the software life cycle: A systematic literature review—[PERSON] et al. on 2020 & [41] \\ \hline PS05 & Software project risk analysis using Bayesian networks with causality constraints—[PERSON] et al. on 2013 & [42] \\ \hline PS06 & Prediction of Risk Percentage in Software Projects by Training Machine Learning Classifiers—[PERSON], [PERSON] on 2021 & [43] \\ \hline PS07 & An integrative framework for intelligent software project risk planning—[PERSON] et al. on 2013 & [44] \\ \hline PS08 & Software Risk Prediction Based on the Hybrid Algorithm of Genetic Algorithm and Decision Tree—[PERSON] et al. on 2007 & [45] \\ \hline PS09 & Prediction of project problem effects on software risk factors—[PERSON] et al. on 2013 & [46] \\ \hline PS10 & Prioritizing Software Components Risk: Towards a Machine Learning-based Approach—[PERSON] et al. on 2020 & [47] \\ \hline PS11 & An integrated approach using IF-TOPSIS, fuzzy DEMATEL, and enhanced CSA optimized ANFIS for software risk prediction—[PERSON], [PERSON] on 2021 & [48] \\ \hline PS12 & Research on Software Project Risk Assessment Model based on Fuzzy Theory and Improved—[PERSON] et al. on 2017 & [49] \\ \hline PS13 & Design and Development of Machine Learning Technique for Software Project Risk Assessment—A Review—[PERSON] et al. on 2020 & [50] \\ \hline PS14 & A Risk Assessment Method based on Software Behavior—[PERSON] et al. on 2019 & [51] \\ \hline PS15 & A Probabilistic Software Risk Assessment and Estimation Model for Software Projects—[PERSON], [PERSON] on 2015 & [18] \\ \hline PS16 & Risk Group Prediction of Software Projects Using Machine Learning Algorithm—[PERSON] on 2021 & [52] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Selected PS with references. ISPRP). IF-ISPRP consists of two fundamental elements: the risk analysis module and the risk planning module. The risk analysis module predicts project success. It creates a cost-effective set of risk control activities from the risk analysis results. They suggested a breakthrough MMAKD approach for complex risk planning. They also utilized the framework to decrease project risk in Guangzhou Wireless City, a social media platform. Other social software projects might help from the risk-management practices discussed here. They believed that integrating risk analysis and planning would help project stakeholders manage project risks. As part of their effort to describe and explain the present level of knowledge on this topic, [PERSON] et al. [41] conducted a comprehensive review of the literature on software risk to identify any gaps or areas that may need future investigation. The findings of their SLR revealed that the scientific community's emphasis had migrated away from the concept of research effort addressing an integrated risk management process and toward work concentrating on specific activities within this process, according to their analysis of the data. It was also feasible to observe an obvious lack of scientific integrity in the validation procedures of the various studies, as well as a weakness in the use of standards or de facto models to characterize the results of these. In the paper of [PERSON] et al. [42], a new model for risk analysis of software development projects based on Bayesian networks with causality restrictions were proposed by the authors (BNCC).