CloudScent: a model for code smell analysis in open-source cloud

TL;DR

CloudScent is an open-source model designed to detect prevalent code smells in open-source cloud environments, demonstrated on OpenStack, aiding in timely refactoring and improving cloud code quality.

Contribution

The paper introduces CloudScent, a novel model for detecting code smells specifically in open-source cloud platforms, addressing a gap in cloud code quality analysis.

Findings

CloudScent accurately detects 8 code smells in cloud environments.

Experiments conducted on OpenStack validate the model's effectiveness.

The model helps cloud providers identify and refactor problematic code smells.

Abstract

The low cost and rapid provisioning capabilities have made open-source cloud a desirable platform to launch industrial applications. However, as open-source cloud moves towards maturity, it still suffers from quality issues like code smells. Although, a great emphasis has been provided on the economic benefits of deploying open-source cloud, low importance has been provided to improve the quality of the source code of the cloud itself to ensure its maintainability in the industrial scenario. Code refactoring has been associated with improving the maintenance and understanding of software code by removing code smells. However, analyzing what smells are more prevalent in cloud environment and designing a tool to define and detect those smells require further attention. In this paper, we propose a model called CloudScent which is an open source mechanism to detect smells in open-source cloud. We test our experiments in a real-life cloud environment using OpenStack. Results show that CloudScent is capable of accurately detecting 8 code smells in cloud. This will permit cloud service providers with advanced knowledge about the smells prevalent in open-source cloud platform, thus allowing for timely code refactoring and improving code quality of the cloud platforms.