On Measuring Software Complexity

System research and information technologies

The use of code metrics allows software developers and project managers to evaluate various features of the software (to be built or already in existence), predict workload, determine software complexity and reliability, and quantify the quality of software systems being developed. Articles written in recent years have proposed various methods for solving this problem. However, there is still no very effective approach to measuring software complexity. This article provides a brief overview of existing software complexity metrics and proposes a new hybrid method for computing software complexity. The proposed hybrid method for evaluating software complexity combines the key features of the Halsted, Maccabe, and SLOC metrics and also allows for a more efficient assessment of complexity.

Communications of the ACM, 1986

There are different sources of software complexity. A large set of complexity metrics can measure distinct program attributes to show different program size indicators. Nevertheless, the size of a program must be obtained from the overall program complexity based on the values of all program factors. Based on the concept that software complexity is a measurement of the resources expended through the software life cycle, and the fact that a program may be approached from three distinct perspectives, the complexity factors are classified into three complexity domains: syntactical, functional, and computational. Each domain represents the magnitudes of the factors in one of the three dimensions: length, time, and level or depth. Using these ideas, in this article we define ordinal measures of the complexity factors based on discrete mathematical structures of programs and the information content or entropy. transform the different domains of software complexity in linear metric spaces in order to represent a program by a set of vectors whose magnitudes and distances represent metrics of the program components, and define a "unified complexity metric" of the program size and the effort needed to produce it over the multilinear complexity space conformed by the three complexity spaces. These metrics may be used to define a statistical method that estimates the size of a program and the effort needed to produce it from the external system design, the productivity in software projects, and the quality and value of software products.

2000

Complexity metrics play an important role in software development; they are reducing the costs during almost the whole development process. There is a growing demand for measuring the complexity of large systems with keeping the consistency of the results regardless of the diversity of the programming languages. In this article we present a general software measurement process on .NET basis

Sigplan Notices, 1996

A different view on software complexity based on long range correlations is presented in this paper. Four different methods are proposed first and then used in the analysis of twenty FORTRAN programs. All methods being used indicate the presence of long range correlations, but every method gives different results. The presence of long range correlations denotes that we can in fact regard the complexity of computer programs from a different perspective.

Software measurement is very important and complex issue, especially when dealing applications into the practice. This paper discusses the comparison of various procedural oriented metrics which are used for the measurement of several properties of source code such as size, complexity, and error. It is widely accepted that sizing or predicting the volume of software process is one of the most dominant aspects of cost estimating. Generally cost estimation model are based upon the strong assumption that size and complexity influences development effort and still there is no unique metric which can work during all of the development phases. In order to improve the software quality and the maintainability, it is necessary to control the software complexity by measuring the related aspects.

People demand for software quality is growing increasingly, thus different scales for the software are growing fast to handle the quality of software. The software complexity metric is one of the measurements that use some of the internal attributes or characteristics of software to know how they effect on the software quality. In this paper, we cover some of more efficient software complexity metrics such as Cyclomatic complexity, line of code and Hallstead complexity metric. This paper presents their impacts on the software quality. It also discusses and analyzes the correlation between them. It finally reveals their relation with the number of errors using a real dataset as a case study.

Computers & Structures, 1994

A method of computing software structural complexity is presented. The parameters and functions to perform this computation are introduced.

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