A metrics-based comparative study on object-oriented programming languages

Abstract

Abstract-There has been a long debate on which programming language can help write better object-oriented programs. However, to date little response is given to this issue with empirical evidence. In this paper, we perform a comparative study on C++, C#, and Java programs by using object-oriented metrics, which comprise measures for class size, complexity, coupling, cohesion, inheritance, encapsulation, polymorphism, and reusability. Our experiment is conducted on 78 tasks in Rosetta Code, a code repository providing solutions to the same programming tasks in different languages. The experimental results show that: (1) C++ classes are significantly larger than C# and Java classes in size, but their complexity does not differ significantly; (2) C# classes are significantly more likely to be coupled than C++ and Java classes through inter-class method invocations instead of direct data access; (3) C# and Java classes tend to be more cohesive than C++ classes; (4) C# and Java significantly outperform C++ in building deep inheritance trees; and (5) programs written in C++, C#, and Java do not show a significant difference in class encapsulation, polymorphism, and reusability. These findings could help practitioners choose suitable languages to develop object-oriented systems.

A VF-IMF Cohesion Metric for Object-Oriented Classes

“…The authors defined LCOM metric as measure of cohesion which is the difference of null intersections with non-null intersections of methods in module and concluded that higher cohesion results in higher quality. The authors later revised their definition of LCOM to incorporate Loose and Tight class cohesions (Wu et al, 2015;Kerdoudi et al, 2016), where LCC is the fraction of number of direct connections and number of indirect connections with maximum number of possible connections and TCC is the fraction of number of direct connections with maximum number of possible connections. Goel and Gupta (2017) propose a dynamic approach to measure cohesion of software systems.…”

Finding Bad Code Smells with Neural Network Models

“…They have identified empirical thresholds for object-oriented metrics by conducting case studies on object-oriented programs [8].As code size increases, automatic detection tools [9] are needed to help the developer by finding code smells systematically. Automatic software tools [10] have been introduced for visually locating code smells in source code by highlighting suspicious code snippets. They allow the developer to apply code refactoring techniques for identified code smells.…”