## 5 Conclusions  
The current research validated the generalizability of the original study's findings in a broader scenario. While the original study used MLP classifiers and a single project dataset based on a single programming language, the present research demonstrated the generalizability of the cost-sensitive approach across multiple projects with multiple languages within the same organization. The NASA MDP datasets were used because they belong to the same organization and involve various languages. Although it was in a specific scenario, expanding the generalizability comprehension of the cost-sensitive approach in this domain is one of the present research's relevant contributions since it demonstrates that the approach can potentially be useful and reliable across various situations involving distinct projects, languages, and teams inside the same organization, indicating that it can be effective in practical applications. However, more investigation into broader generalization scenarios is needed, such as validating the technique on projects from different organizations (cross-organizational validation). Aiming to address the issues pointed out by [29], performance evaluation metrics more specific to the context of the software testing domain were refined (from the previous study) and proposed. Furthermore, although the study aimed to validate that the ML model can be interchangeable between languages, a very small number of languages were used, and they have similarities (C, C++, Java). Despite these similarities, Java has considerable differences, for example, in memory management and exception handling, indicating promising results in generalization across different languages but cannot be generalized without further studies. Therefore, new explorations with additional software development projects based on other programming languages with more significant structural differences should be the target of future investigations. Validating the results for RF is an important contribution since RF has advantages over MLP, the ML approach used in the original study. It is more adaptable to different data types and manages missing values better. Also, it requires less data preprocessing, such as data scaling. It can efficiently process large datasets, requiring less computation, making its training process orders of magnitude faster and cheaper than MLP or more complex ANNs. It offers very rapid predictions after training. It is suitable for solving complex nonlinear relationships between the target and independent variables. Moreover, as demonstrated in this study, where default parameters were used, RF can achieve good results, even without good hyperparameter tuning, which are often comparable to those achieved by well-tuned MLPs. Therefore, RF usage reduces the need for an ML expert. Finally, RF can achieve high accuracies even with smaller data samples, which is crucial to the present application domain since many software development projects are small or medium, and there may not be a high volume of historical data about static source-code metrics and defects. Thus, RF can reduce the entry barrier for software testing informed by ML models. However, it is noteworthy that although RFs tend to be more resistant to overfitting compared to individual DTs or MLPs because they average multiple decision trees and introduce randomness through bootstrap sampling and feature selection, they are not entirely immune to it. Thus, additional explorations using different datasets with possible distinct noise patterns and ML techniques to validate the current study results will be performed in future studies. Although the proposed approach uses ML model prediction to inform decision-making on software testing scoping, an essential aspect of ML is the potential value added with its explainability and interpretability. By using RF rather than MLP, the present study also gave an additional step towards a better explainability and interpretability of the cost-sensitive trained models.