A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Ardito, Luca; Coppola, Riccardo; Barbato, Luca; Verga, Diego

doi:10.1155/2020/8840389

Cited by 17 publications

(10 citation statements)

References 45 publications

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“…Through calculated quality numeric values, the study established specific relationship between each quality attribute and how it affects the overall quality of the software system. Earlier research efforts have focused on quantification of a single quality attribute [28][29][30][31], but this study demonstrated the quantification of key software quality attributes that summarises the quality of the software. This paper, therefore, laid emphasis on the characteristics of software products that can unleash significant information about the quality of the products.…”

Section: Discussionmentioning

confidence: 89%

Quantifying Software Quality in Agile Development Environment

Ikerionwu¹,

Caesar²

2021

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Due to required efforts and the challenges involved in understanding the quantification of software quality, researchers have chosen varying quality attributes to describe the quantification of software quality. The degree of software quality is achieved from the standards and quality attributes at each development process: the adherence of software engineering principles towards realizing a product of good quality. In agile environment, the software engineering process ensures that qualities of interest are built-in and to produce software product with an acceptable level of quality. Thus, this study is aimed at quantifying six related software quality attributes. The specific objectives include identifying the software quality attributes, the design of the algorithm for measurement metrics, and to perform relational analytics of each attribute with respect to the software quality. The methodology followed an exploratory evaluation of measurement and metrics and their role in quantifying software quality in agile development environment. The study adopted existing metrics to quantify software quality attributes. Twelve opensource software projects were tested for 6 specific quality attributes and each result is quantified and presented. Results show that software number 2 (SW2) has a maintainability value of 6 minutes, 50% availability, and 0.62 reliability values. It implies that a high value of maintainability does not translate to high reliability. These values establish the relationship between attributes and enhances developers and users' understanding of the software quality and its attributes.

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Section: Discussionmentioning

confidence: 89%

Quantifying Software Quality in Agile Development Environment

Ikerionwu¹,

Caesar²

2021

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“…In this study, software metrics have been imported from the BugHunter Dataset and re-validated using MetricsReloaded [47], which is a tool for obtaining/measuring software metrics from source codes. The software metrics we used in this study are at the class level and can be found in Table 2.…”

Section: Extract Software Metricsmentioning

confidence: 99%

Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

2023

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In software development, identifying software faults is an important task. The presence of faults not only reduces the quality of the software, but also increases the cost of development life cycle. Fault identification can be performed by analysing the characteristics of the buggy source codes from the past and predict the present ones based on the same characteristics using statistical or machine learning models. Many studies have been conducted to predict the fault proneness of software systems. However, most of them provide either inadequate or insufficient information and thus make the fault prediction task difficult. In this paper, we present a novel set of software metrics called Error-type software metrics, which provides prediction models with information about patterns of different types of Java runtime error. Particular, in this study, the ESM values consist of information of three common Java runtime errors which are Index Out Of Bounds Exception, Null Pointer Exception, and Class Cast Exception. Also, we proposed a methodology for modelling, extracting, and evaluating error patterns from software modules using Stream X-Machine (a formal modelling method) and machine learning techniques. The experimental results showed that the proposed Error-type software metrics could significantly improve the performances of machine learning models in fault-proneness prediction.

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“…The study focuses on Java and C# applications for object-oriented programming and covers the findings of these languages. They are frequently used as they are the most compatible with the tools analyzed for the maintainability of the software [27] and are preferred in energy efficiency studies [28,29]. These two languages have similar syntax, and applications with common goals written in these two languages show similar characteristics in terms of class encapsulation, polymorphism, and reusability [30].…”

Section: Data Collectionmentioning

confidence: 99%

Energy Efficiency Analysis of Code Refactoring Techniques for Green and Sustainable Software in Portable Devices

2022

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Code refactoring is a time-consuming and effort-intensive process that is applied for making improvements to source codes. There exist several refactoring techniques to improve software quality. Some of them aim to reduce the energy consumption of the software. However, the combination of applied refactoring techniques is crucial to the success rate. In addition, to provide sustainable services on portable devices such as mobile phones and laptops, which rely on batteries, improving and optimizing the energy efficiency is important. This study focuses on examining the effect of code refactoring techniques on energy consumption. A total of 25 different source codes of applications programmed in the C# and Java languages are selected for the study, and combinations obtained from refactoring techniques are applied to these source codes. The combinations applied are analyzed using the maintainability index. Power consumption estimation tools are used to measure the energy consumption of the original and refactored codes. The results show that the combinations significantly improve the software’s energy efficiency. The results will provide a better understanding of the relationship between the energy efficiency of software and refactoring techniques. Moreover, they will help developers to improve their object-oriented code in terms of both energy efficiency and sustainability.

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A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Cited by 17 publications

References 45 publications

Quantifying Software Quality in Agile Development Environment

Quantifying Software Quality in Agile Development Environment

Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

Energy Efficiency Analysis of Code Refactoring Techniques for Green and Sustainable Software in Portable Devices

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