They differ only in determining which situations the relationship between methods and attributes, which are the basic building block of the class, exist and in which situations they do not.

In the cohesion metric formulas used, the number of methods and the number of attributes is necessarily common. The extent to which the variables created in a class are used by the methods created in that class directly affects the cohesion value of that class. In order to calculate the cohesion value of a class, almost all of the metrics used in the literature considered the methods and attributes that form the basis of the class and their relationship with each other. In this study, LCOM2, TCC, LCC, and LSCC metric values were predicted by only using the number of methods (a procedure associated with a message and an object) and attributes. However, out of all the metrics, RF, REPTree, and KNN had close performances with each other, and therefore any of the RF, REPTree, and KNN techniques can be used for software cohesion metric prediction. According to the obtained results, for the LCOM2 and TCC metrics, the KNN algorithm provided the best results, and for LCC and LSCC metrics, the REPTree algorithm was the best. We created two datasets utilizing two different open-source software projects. In this study, besides the use of these tools, we predicted the cohesion values (LCOM2, TCC, LCC, and LSCC) with machine learning techniques (KNN, REPTree, multi-layer perceptron, linear regression (LR), support vector machine, and random forest (RF)) to solve them alternatively. The user should apply different tools for different metrics.

Although there are many static code analysis tools, not every tool measures every metric.

However, measuring the cohesion value is a relatively difficult issue when tracing the source code manually.

The cohesion value is one of the important factors used to evaluate software maintainability.

Open source vector design software