Empirical study on refactoring large-scale industrial systems and its effects on maintainability

Szke, Gbor; Antal, Gábor; Nagy, Csaba; Ferenć, Rudolf; Gyimthy, Tibor

doi:10.1016/j.jss.2016.08.071

Cited by 32 publications

(18 citation statements)

References 15 publications

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“…The research in [20] main objective was: Can refactoring improve maintainability? His study focused on applying refactoring methods on six large projects software.…”

Section: Related Workmentioning

confidence: 99%

Impact of Refactoring on OO Metrics: A Study on the Extract Class, Extract Superclass, Encapsulate Field and Pull up Method

Alazzam¹,

Abuata²,

Mhediat³

2020

IJMLC

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Refactoring is the key to improve software maintainability, reduce complexity and get clear code with the ability to understand and modify it in efficient way. In this paper we present four refactoring techniques which are: Extract Class, Extract Superclass, Encapsulate Field and Pull up Method to discover their effect on multi Object Oriented metrics and factors. The main objective of this study is to help the developers and maintenance engineers in choosing the best possible refactoring technique based on determined goals. The results demonstrate that the types of refactoring techniques have distinctive impact on the metrics values. The results show that all contemplated refactoring techniques increase the Cyclomatic number value (VG). However, the Encapsulate Field has no impact on: Coupling between Objects, Depth of Inheritance, Number of children and Outward Coupling metrics. Index Terms-Refactoring techniques, encapsulate field, pull up method.

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“…The research in [20] main objective was: Can refactoring improve maintainability? His study focused on applying refactoring methods on six large projects software.…”

Section: Related Workmentioning

confidence: 99%

Impact of Refactoring on OO Metrics: A Study on the Extract Class, Extract Superclass, Encapsulate Field and Pull up Method

Alazzam¹,

Abuata²,

Mhediat³

2020

IJMLC

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“…Although software re-engineering has been widely studied, for example in several EU funded projects such as [77], it is a time consuming and complex process. Empirical study of industrial software refactoring [78] indicates that developers tend to fix concrete coding issues and improve the maintainability of their code. Because energy-efficiency is largely invisible to the developers, it is unlikely that it would be considered in ordinary refactoring.…”

Section: Most Of the Work Deals With How New And Changing Functionalmentioning

confidence: 99%

Towards Green Big Data at CERN

Niemi

Nurminen

Liukkonen³

et al. 2018

Future Generation Computer Systems

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High-energy physics studies collisions of particles traveling near the speed of light. For statistically significant results, physicists need to analyze a huge number of such events. One analysis job can take days and process tens of millions of collisions. Today the experiments of the large hadron collider (LHC) create 10 GB of data per second and a future upgrade will cause a tenfold increase in data. The data analysis requires not only massive hardware but also a lot of electricity. In this article, we discuss energy efficiency in scientific computing and review a set of intermixed approaches we have developed in our Green Big Data project to improve energy efficiency of CERN computing. These approaches include making energy consumption visible to developers and users, architectural improvements, smarter management of computing jobs, and benefits of cloud technologies. The open and innovative environment at CERN is an excellent playground for different energy efficiency ideas which can later find use in mainstream computing.

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“…In an earlier work [17] we analyzed the eect of code refactorings by automatically extracting refactorings using the RefFinder [21] During an in-vivo industrial experiment [38], we showed that bulk-xing coding issues found by various code linters can lead to a quality increase. The drawback of this approach was that developers tend to perform the easiest code changes, which typically were not even classical Fowler refactorings.…”

Section: Introductionmentioning

confidence: 99%

A Hands-on OpenStack Code Refactoring Experience Report

Antal

Szarka

Hegedűs

2018

Computational Science and Its Applications – ICCSA 2018

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Nowadays, almost everyone uses some kind of cloud infrastructure. As clouds gaining more and more attention, it is now even more important to have stable and reliable cloud systems. Along with stability and reliability comes source code maintainability. Unfortunately, maintainability has no exact denition, there are several denitions both from users' and developers' perspective. In this paper, we analyzed two projects of OpenStack, the world's leading open-source cloud system, using QualityGate, a static software analyzer which can help to determine the maintainability of software. During the analysis we found quality issues that could be xed by refactoring the code. We have created 47 patches in this two OpenStack projects. We have also analyzed our patches with QualityGate to see whether they increase the maintainability of the system. We found that a single refactoring has a barely noticeable eect on the maintainability of the software, what is more, it can even decrease maintainability. But if we do refactorings regularly, their cumulative eect will probably increase the quality in the mid and long-term. We also experienced that our refactoring commits were very appreciated by the open-source community.

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Empirical study on refactoring large-scale industrial systems and its effects on maintainability

Cited by 32 publications

References 15 publications

Impact of Refactoring on OO Metrics: A Study on the Extract Class, Extract Superclass, Encapsulate Field and Pull up Method

Impact of Refactoring on OO Metrics: A Study on the Extract Class, Extract Superclass, Encapsulate Field and Pull up Method

Towards Green Big Data at CERN

A Hands-on OpenStack Code Refactoring Experience Report

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