2006Unpublished venueRequires access

A Complexity Metrics Set for Large-Scale Object-Oriented Software Systems

Yutao Ma, Keqing He, Dehui Du, Jing Liu, Yulan Yan

Open publisher page 39 citations

Abstract

Although traditional software metrics have widely been applied to practical software projects, they have insufficient abilities to measure a large-scale system's complexity at high level so as to provide an overview of the system for developers. So, an adequate metrics set for large-scale software systems that can comprehensively measure the complexity at various levels is still challengeable. First, we summarize universal properties and implicit limitations of recognized object-oriented metric sets in the face of ever-increasing complexities of modern software systems. Large-scale software systems represent an important class of artificial complex networks. Then, from the perspective of software engineering, the main parameters of complex networks are introduced in detail. Furthermore, we integrate these metrics and parameters into a hierarchical complexity metrics set, which can measure the complexity at different levels of a large-scale software system. Eventually, we prove the feasibility of our metrics set through analyzing the data from a software project.

About this research paper

What this paper is about

Although traditional software metrics have widely been applied to practical software projects, they have insufficient abilities to measure a large-scale system's complexity at high level so as to provide an overview of the system for developers. So, an adequate metrics set for large-scale software systems that can comprehensively measure the complexity at various levels is still challengeable. First, we summarize universal properties and implicit limitations of recognized object-oriented metric sets in the face of ever-increasing complexities of modern software systems. Large-scale software systems represent an important class of artificial complex networks. Then, from the perspective of software engineering, the main parameters of complex networks are introduced in detail. Furthermore, we integrate these metrics and parameters into a hierarchical complexity metrics set, which can measure the complexity at different levels of a large-scale software system. Eventually, we prove the feasibility of our metrics set through analyzing the data from a software project.

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OpenAlex reports 39 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Although traditional software metrics have widely been applied to practical software projects, they have insufficient abilities to measure a large-scale system's complexity at high level so as to provide an overview of the system for developers. So, an adequate metrics set for large-scale software systems that can comprehensively measure the complexity at various levels is still challengeable. First, we summarize universal properties and implicit limitations of recognized object-oriented metric sets in the face of ever-increasing complexities of modern software systems. Large-scale software systems represent an important class of artificial complex networks. Then, from the perspective of software engineering, the main parameters of complex networks are introduced in detail. Furthermore, we integrate these metrics and parameters into a hierarchical complexity metrics set, which can measure the complexity at different levels of a large-scale software system. Eventually, we prove the feasibility of our metrics set through analyzing the data from a software project.

Key concepts: Software metric, Computer science, Software sizing, Software system, Software measurement, Software construction, Programming complexity, Software

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