Predicting the Impact of Batch Refactoring Code Smells on Application Resource Consumption

TL;DR

This paper investigates how batch refactoring of code smells affects application resource consumption and develops predictive models to guide resource-aware refactoring decisions.

Contribution

It provides an empirical analysis of resource impacts from refactoring 16 code smell types and introduces ANN-based models to predict resource changes before refactoring.

Findings

Refactoring impacts CPU and memory utilization variably across code smells.

ANN regression models accurately predict resource consumption changes.

Resource-aware refactoring can optimize code quality without increasing resource use.

Abstract

Automated batch refactoring has become a de-facto mechanism to restructure software that may have significant design flaws negatively impacting the code quality and maintainability. Although automated batch refactoring techniques are known to significantly improve overall software quality and maintainability, their impact on resource utilization is not well studied. This paper aims to bridge the gap between batch refactoring code smells and consumption of resources. It determines the relationship between software code smell batch refactoring, and resource consumption. Next, it aims to design algorithms to predict the impact of code smell refactoring on resource consumption. This paper investigates 16 code smell types and their joint effect on resource utilization for 31 open source applications. It provides a detailed empirical analysis of the change in application CPU and memory utilization after refactoring specific code smells in isolation and in batches. This analysis is then used to train regression algorithms to predict the impact of batch refactoring on CPU and memory utilization before making any refactoring decisions. Experimental results also show that our ANN-based regression model provides highly accurate predictions for the impact of batch refactoring on resource consumption. It allows the software developers to intelligently decide which code smells they should refactor jointly to achieve high code quality and maintainability without increasing the application resource utilization. This paper responds to the important and urgent need of software engineers across a broad range of software applications, who are looking to refactor code smells and at the same time improve resource consumption. Finally, it brings forward the concept of resource aware code smell refactoring to the most crucial software applications.