## 2 Materials and Methods
### Machine Learning Framework and Technique  
Although advanced cutting-edge ML techniques with excellent hyperparameter optimization running on sophisticated ML frameworks requiring coding skills could potentially achieve superior performance, they reduce the intention of software testing professionals to adopt AI-based solutions by negatively impacting some of the factors influencing their intention to do so: facilitating conditions, expected effort, and self-efficacy [70]. Since software testers and their managers are not likely to be experts in ML, using a codeless ML framework and a more straightforward ML technique that could achieve good performance even without hyperparameter optimization (i.e., using default settings) were requirements. Therefore, the ML framework used was WEKA (Waikato Environment for Knowledge Analysis) version 3.8.6 [71]. WEKA is a popular, versatile, and accessible open-source ML and data-mining framework developed at the University of Waikato in New Zealand. It provides a Graphical User Interface for a codeless environment encompassing a collection of visualization tools and algorithms for data analysis and predictive modeling for anyone interested in data mining and machine learning, from beginners to advanced practitioners. Regarding the ML technique, although ANNs are very popular, diverse, and powerful, they have essential disadvantages in applications related to the current study's domain, as previously mentioned. Thus, unlike the ANN approach used in the original study [40] to avoid the weaknesses of ANNs, the present research used Random Forest (RF) as the ML technique to induce the ML models. RF is a decision tree-based ML technique using the ensemble method principle by averaging multiple DTs to improve predictive accuracy and control overfitting. This approach leverages the strengths of multiple DTs, each trained on random subsets of the data and features, to produce a more robust model than any single tree could offer. RF significantly reduces the variance without substantially increasing bias by aggregating the predictions from many trees through majority voting for classification tasks or averaging for regression tasks [72]. RFs have many advantages over ANNs. RF requires smaller datasets than ANNs to perform similarly, making the ML approach more suitable for situations with limited data availability [73]. It also requires shorter training times and less advanced hardware for training [74]. Unlike ANNs, RFs can handle categorical and numerical data without extensive preprocessing or feature scaling and require much simpler hyperparameter tuning, not requiring highly specialized staff to use them [73]. Although hyperparameter optimization has important implications for the performance of the ML model, since RF is less dependent on it and ML technique simplicity was a study requirement, the RF classifier default settings in the WEKA framework were used in all experiments in the present study. The underlying idea was that if the cost-sensitive approach using RF with default hyperparameter values could reach good results using data from multiple in-company projects with some distinct programming languages, that would be compelling enough since it could indicate that even superior results could be achieved with proper hyperparameter tuning. Additionally, that would demonstrate software testing professionals without much experience in ML would be able to use it in practical applications. Moreover, RF is less prone to overfitting than ANNs because of the ensemble method of averaging multiple DTs, which leads to better generalization by reducing variance, which is essential in the investigated domain [72]. Finally, although RFs are not entirely white-box, they have higher explainability and interpretability than ANNs since their induced decision paths through the trees can be examined [75].