## 4 Design
### Dataset
#### 4.1.3 Feature Interaction  
Feature interaction, which encompasses the relationships and combined effects among various features within a dataset, is pivotal in capturing these complex patterns and deep relationships. This capability is instrumental in enhancing the model's learning efficacy. Therefore, by evaluating the degree of correlation between the variables in the dataset constructed in this work, we find that the complexity of feature-complex interactions is reflected in multiple levels, including order-1 interactions, denoting the direct relationships between individual features; order-2 interactions, involving the interaction between feature pairs; and high-order interactions, which cover interactions between three or more features. The interactions between these features will be explored in more detail next. We first analyze the order-1 interactions of the features, as shown in Figure 3, which presents a heatmap of several feature pairs. A heatmap, as a representation of correlation coefficients, can map correlation values to a color scale. The correlation coefficient reflects the strength and direction of the linear relationship between two features. When the correlation coefficient is close to +1, it signifies a strong positive correlation, i.e., they tend to increase synchronously, and the heatmap color tends to be red. Conversely, when the correlation coefficient is close to \(-1\), it indicates that there is a strong negative correlation, i.e., one feature decreases as the other increases, and the heatmap color tends to be blue. In addition, if the correlation coefficient is close to 0, it means that there is hardly any linear relationship between the two features, and the color in the heatmap is white or pale. Figure 3: Heatmap of order\(-1\) feature interactions. Four large red areas are prominent in the figure. The correlations among seven metrics (CBO, CBO Modified, FAN-IN, FAN-OUT, WMC, RFC, and LCOM) range between 0.7 and 1 in the first region. For example, the correlation coefficients between CBO and FAN-IN, FAN-OUT, WMC, and RFC are 0.96, 1.00, 0.82, and 0.97, respectively. This indicates that, as CBO increases, FAN-IN, FAN-OUT, WMC, and RFC tend to increase as well, suggesting the potential existence of some form of synergy or a common growth trend among these features. The correlation coefficients between FAN-IN and FAN-OUT and between WMC and RFC are 0.96 and 0.73, respectively, both showing strong positive correlations. This implies that an increase in FAN-IN is accompanied by an increase in FAN-OUT, WMC, and RFC, further supporting the monotonic increasing relationship between these features. Notably, the correlation coefficients between CBO, CBO Modified, and FAN-OUT are 1.00, indicating that their changes are perfectly aligned. This suggests the possibility of a close relationship or a shared driving factor among these three features. The same analysis can be performed in the second and third regions, showing that these seven metrics, along with privateMethodsQty, defaultMethodsQty, visibleMethodsQty, NOSI, LOC, and assignmentsQty, have correlation values ranging from 0.72 to 1. In the fourth region, we observe strong correlations among six metrics--privateMethodsQty, defaultMethodsQty, visibleMethodsQty, NOSI, LOC, and assignmentsQty--with correlation values ranging from 0.79 to 1. Additionally, two distinct regions of importance appear in the graph: one near the partition line and the other along the edge. The region along the partition line displays the correlations between totalMethodsQty, publicMethodsQty, and the 13 metrics mentioned above, with values between 0.73 and 0.99. The edge region highlights a strong negative correlation between modifiers, totalMethodsQty, and these 13 metrics, with values from \(-\)0.72 to \(-\)1. For example, the correlation coefficients between modifiers and CBO, CBO Modified, FAN-IN, and FAN-OUT are \(-\)0.96, \(-\)0.97, \(-\)1.00, and \(-\)0.96, respectively, indicating a strong negative correlation.