Dynamic metrics are superior than static metrics in maintainability prediction: An empirical case study

Abstract

Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions like budget planning, cost estimation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments. Many design metrics have proposed in literature to measure various constructs of Object Oriented (OO) paradigm such as class, coupling, cohesion, inheritance, information hiding and polymorphism and use them further in determining the various aspects of software quality. However, the use of conventional static metrics have found to be inadequate for modern OO software due to the presence of run time polymorphism, templates class, template methods, dynamic binding and some code left unexecuted due to specific input conditions. This gap gave a cue to focus on the use of dynamic metrics instead of traditional static metrics to capture the software characteristics and further deploy them for maintainability predictions. As the dynamic metrics are more precise in capturing the execution behavior of the software system, in the current empirical investigation with the use of open source code, we validate and verify the superiority of dynamic metrics over static metrics. Four machine learning models are used for making the prediction model while training is performed simultaneously using static as well as dynamic metric suite. The results are analyzed using prevalent prediction accuracy measures which indicate that predictive capability of dynamic metrics is more concise than static metrics irrespective of any machine learning prediction model. Results of this would be helpful to practitioners as they can use the dynamic metrics in maintainability prediction in order to achieve precise planning of resource allocation.