An evaluation of the MOOD set of object-oriented software metrics

Harrison, Rachel; Counsell, Steve; Nithi, R.

doi:10.1109/32.689404

Cited by 262 publications

(133 citation statements)

References 22 publications

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“…There are several metrics proposed in the literature for capturing the quality of ObjectOriented (OO) design and code, for example, ([Aggarwal05]; [Braind98] [Braind99]; [Bieman95]; [Cartwright00]; [Chidamber94][Chidamber91]; [Harrison98]; [Henderson96]; [Hitz00]; [Lake94]; [Li93]; [Lee95]; [Lorenz94]; [Tegarden95]). These metrics provide ways to evaluate the quality of software and their use in earlier phases of software development can help organizations in assessing large software development quickly, at a low cost [Braind99].…”

Section: Introductionmentioning

confidence: 99%

“…Empirical studies of real systems can provide relevant answers. There have been few empirical studies evaluating the effect of object-oriented metrics on software quality and constructing models that utilize them in predicting quality attributes in the system, such as (Basili96] [Binkley98]; [Braind00][Braind01]; [Cartwright00]; [Chidamber98]; [Emam99][Emam01]; [Gyimothy05]; [Harrison98]; [Li93]; [Ping02]). …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigating effect of Design Metrics on Fault Proneness in Object-Oriented Systems.

Aggarwal¹,

Singh²,

Kaur³

et al. 2007

JOT

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Demand for quality software has undergone with rapid growth during the last few years. This is leading to an increase in the development of metrics for measuring the properties of software such as coupling, cohesion or inheritance that can be used in early quality assessments. Quality models that explore the relationship between these properties and quality attributes such as fault proneness, maintainability, effort or productivity are needed to use these metrics effectively. The goal of this work is to empirically explore the relationship between object-oriented design metrics and fault proneness of object-oriented system classes. The study used data collected from Java applications is containing 136 classes. We use a set of twenty-six design metrics in our work. Result of this study shows that many metrics are based on comparable ideas and provide redundant information. It is shown that by using a subset of metrics in the prediction models can be built to identify the faulty classes. The proposed model predicts faulty classes with more than 80% accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating effect of Design Metrics on Fault Proneness in Object-Oriented Systems.

Aggarwal¹,

Singh²,

Kaur³

et al. 2007

JOT

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“…The deeper the class in the hierarchy, the greater the number of methods it is likely to inherit. MOOD metrics [8] states class hierarchy as Method Inheritance Factor (MIF) and Attribute Inheritance Factor (AIF). Lorenz and Kidd [9] proposed class hierarchy as Number of Operations Added by a subclass (NOA).…”

Section: Class Hierarchy Of Object-oriented Software Systemsmentioning

confidence: 99%

“…Basili [4], Briand [5], Li [6], Tang [7] made empirical, theoretical, real-time system study on CK Metric suite. Harrison, Counsell and Nithi [8] in MOOD metric suite proposedmetrics on method and attribute hiding factor, inheritance factor, polymorphism and coupling factor. Lorenz and Kidd [9] proposed class based metrics into size, inheritance, internal and external categories of software development.…”

Section: Introductionmentioning

confidence: 99%

An Access Control Metric Suite for Class Hierarchy of Object-Oriented Software Systems

Chhillar¹,

Kajla²,

Chhillar³

et al. 2015

IJCCE

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Abstract-During last two decades, object-oriented paradigm has arisen as a prevailing software engineering practice for solving software problems. A large number of object-oriented metrics are proposed during that period to measure the properties like abstraction, encapsulation, inheritance, polymorphism, coupling, cohesion, information hiding and reusability. Most of the researchers mainly depict attributes like class, object, and method to measure the properties in their research. In this paper metrics are defined to depict Member Access Control mechanism and then employed in the class hierarchy. By successfully implemented the proposed metrics in object-oriented programing, we quantify the derived classes which directly results to reduce code, time and complexity of the object-oriented software systems. The proposed metrics provides a new way to understand and imply these concepts in research and development of the software using object-oriented approach.

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“…To evaluate the quality of object-oriented programs, many metrics have been proposed, which are related to various language features like class size, coupling, cohesion, inheritance, encapsulation, and polymorphism [4][5][6][7][8][9]. In previous studies, these metrics are generally applied to fault prediction [10], class testability prediction [11], code refactoring [12], and code size estimation [13].…”

Section: Introductionmentioning

confidence: 99%

A metrics-based comparative study on object-oriented programming languages

Chen

Zhou

et al. 2015

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-There has been a long debate on which programming language can help write better object-oriented programs. However, to date little response is given to this issue with empirical evidence. In this paper, we perform a comparative study on C++, C#, and Java programs by using object-oriented metrics, which comprise measures for class size, complexity, coupling, cohesion, inheritance, encapsulation, polymorphism, and reusability. Our experiment is conducted on 78 tasks in Rosetta Code, a code repository providing solutions to the same programming tasks in different languages. The experimental results show that: (1) C++ classes are significantly larger than C# and Java classes in size, but their complexity does not differ significantly; (2) C# classes are significantly more likely to be coupled than C++ and Java classes through inter-class method invocations instead of direct data access; (3) C# and Java classes tend to be more cohesive than C++ classes; (4) C# and Java significantly outperform C++ in building deep inheritance trees; and (5) programs written in C++, C#, and Java do not show a significant difference in class encapsulation, polymorphism, and reusability. These findings could help practitioners choose suitable languages to develop object-oriented systems.

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An evaluation of the MOOD set of object-oriented software metrics

Cited by 262 publications

References 22 publications

Investigating effect of Design Metrics on Fault Proneness in Object-Oriented Systems.

Investigating effect of Design Metrics on Fault Proneness in Object-Oriented Systems.

An Access Control Metric Suite for Class Hierarchy of Object-Oriented Software Systems

A metrics-based comparative study on object-oriented programming languages

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