# Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques
## 5 Results and Discussions
### Description of Primary Studies (PS)
[PERSON] et al. [7] provided a risk dataset comprising the bulk of the risk prediction parameters as well as software needs for the new software requirements. The collection comprises the vast bulk of the requirements derived from the Software Requirement Specification (SRS) of numerous open-source projects (SRS). The study was split up into three primary phases, the first of which was the collecting of data with a risk-oriented focus. The other two phases were the validation of datasets by IT professionals and the filtration of datasets. [PERSON] et al. [51] devised a method for detecting the hazard of a system based on the software behavior of the system's components. The behavior of untrusted software when it calls other untrusted software is intimately related to system risk; specifically, the more and more untrusted software is called, the greater the risk the system faces, and the converse is true. Therefore, illegal computer operation is a subset of system risk, and the two are inversely proportional to each other in terms of likelihood. A quantitative analytical method (HMM) was used to assess the system's risk level because the number and scope of untrusted program calls can be accurately monitored, but their risk level cannot be clearly seen. This method guarantees the objectivity and correctness of the results, and it was used in their article. Also included are experiments to study and explain the risk assessment method based on software behavior, which was carried out as part of the article. [PERSON] et al. [45] devised a hybrid learning approach that employed evolutionary algorithms and decision trees to evolve optimum subsets of software metrics for risk prediction during the early phase of the software life cycle, and this strategy was deployed. When compared to the use of all metrics for decision tree risk prediction, the experimental results indicate the feasibility and enhanced performance of their method. [PERSON] and [PERSON] [43] provided a novel framework for analyzing the dataset gathered through a questionnaire, in which machine learning classifiers were applied and risk assessments were generated for each of the software models that were identified. Software product managers can use the results to select the most appropriate software model based on the software requirements and the probability of risk prediction. [PERSON] et al. [49] used the correlation coefficient to combine historical data based on conceptions such as risk weight, expert trust, and risk consequence, allowing the assessor to measure the impact of risk factors at the macro and micro level. According to the findings of the case study, the model was objective and scientific, realistic, and provided a solid framework for risk prediction, mitigation, and control activities. In the paper of [PERSON] and [PERSON] [48], the Development of a new hybridized fuzzy-based risk assessment framework was used in software projects. During decision-making, the proposed technique discovered and prioritized project dangers. Using intuitionistic fuzzy-based TOPSIS, adaptive neurofuzzy inference system-based multi-criteria decision-making (ANFIS MCDM), and fuzzy decision-making trial and evaluation laboratory processes improved software project risk assessment. An improved crow search method was used to modify ANFIS parameters for a more accurate software risk rating (ECSA). Integrating ANFIS with ECSA led to solutions that stayed inside the local optimum and requiredonly minor ANFIS parameter modifications. NASA's 93 dataset contained 93 software project variables for experimental validation. The experimental results showed that the proposed fuzzy-based framework properly evaluated software development project risks. Quality Assessment  
Here, in Table 4, we now show the results of the QQ questionnaire, which we had previously presented in the paper. It is evident from the data that the great majority of QQs received positive responses to their questions.