57 research outputs found
URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud
The low cost and rapid provisioning capabilities have made the cloud a
desirable platform to launch complex scientific applications. However, resource
utilization optimization is a significant challenge for cloud service
providers, since the earlier focus is provided on optimizing resources for the
applications that run on the cloud, with a low emphasis being provided on
optimizing resource utilization of the cloud computing internal processes. Code
refactoring has been associated with improving the maintenance and
understanding of software code. However, analyzing the impact of the
refactoring source code of the cloud and studying its impact on cloud resource
usage require further analysis. In this paper, we propose a framework called
Unified Regression Modelling (URegM) which predicts the impact of code smell
refactoring on cloud resource usage. We test our experiments in a real-life
cloud environment using a complex scientific application as a workload. Results
show that URegM is capable of accurately predicting resource consumption due to
code smell refactoring. This will permit cloud service providers with advanced
knowledge about the impact of refactoring code smells on resource consumption,
thus allowing them to plan their resource provisioning and code refactoring
more effectively
Qualitative analysis of the relationship between design smells and software engineering challenges
Software design debt aims to elucidate the rectification attempts of the
present design flaws and studies the influence of those to the cost and time of
the software. Design smells are a key cause of incurring design debt. Although
the impact of design smells on design debt have been predominantly considered
in current literature, how design smells are caused due to not following
software engineering best practices require more exploration. This research
provides a tool which is used for design smell detection in Java software by
analyzing large volume of source codes. More specifically, 409,539 Lines of
Code (LoC) and 17,760 class files of open source Java software are analyzed
here. Obtained results show desirable precision values ranging from 81.01\% to
93.43\%. Based on the output of the tool, a study is conducted to relate the
cause of the detected design smells to two software engineering challenges
namely "irregular team meetings" and "scope creep". As a result, the gained
information will provide insight to the software engineers to take necessary
steps of design remediation actions.Comment: arXiv admin note: substantial text overlap with arXiv:1910.0542
- …