Cyclomatic Complexity Metric Research Articles

Analysis of Cognitive Complexity with Cyclomatic Complexity Metric of Software

Analysis of Cognitive Complexity with Cyclomatic Complexity Metric of Software

Multifaceted infrastructure for self-adaptive IoT systems

Abstract Background: Internet of Things (IoT) enables the interaction among objects to provide services to their users. Areas such as eHealth, smart energy, and smart buildings have been benefiting from the IoT potential. However, the development of IoT systems is still complex because it deals with a highly dynamic, volatile, and heterogeneous environment. These characteristics require discovering devices, managing these devices’ context, and self-adapt their behavior. Goal : In this work, we propose a self-adaptive IoT infrastructure to support multiple facets, i.e., the contextual discovery of smart objects, the context management, and the self-adaptation process of the development of these systems. Methods : We evaluated the proposed infrastructure by developing a smart building application with and without it. The evaluation focused on four issues: the feasibility of integrating the context management through middleware platforms with adaptation based on workflows in a request/response communication model, the impact of our infrastructure on the development of self-adaptive IoT systems considering cyclomatic complexity and coupling code metrics; the impact of using contextual filters on the orchestrator of self-adaptation; and the impact on the quality of the self-adaptation. Results : The results suggest that: (i) it is feasible to use the proposed infrastructure in the development of self-adaptive IoT systems; (ii) there is a reduction in the cyclomatic complexity and the coupling with our approach, (iii) there is a considerable decrease in the number of rules evaluated at runtime, (iv) our infrastructure reduces the execution time of the adaptations when using contextual filters, and (v) the self-adaptation process was effective when using the orchestrator of self-adaptations. Conclusion : With these results, we observed that the proposed multifaceted infrastructure could reduce the complexity related to the development of IoT systems, in addition to optimizing their self-adaptation process.

Complexity reduction of the Engineered Safety Features Component Control System

A complexity reduction methodology for the ESF-CCS (Engineered Safety Features- Component Control System) based on system analysis is developed in this work. The primary objective of this work is to demonstrate that the adoption of an FPGA (Field Programmable Gate Array) based architecture results in a decrease in the system complexity. The concepts of complexity and complexity metrics, as well as the ways in which the FPGA platform can result in a decrease in complexity, are defined. Following this, an analysis of the ESF-CCS is performed. The sources of complexity are identified and the system complexity is measured using McCabe’s cyclomatic complexity metric. An FPGA-based design with reduced complexity is then presented. The reduction in complexity is achieved by the use of flat hardware logic, System on Chip architecture, and the separation of logically independent functions. By adopting an FPGA architecture, the control logic can be reduced to around a half. This reduction in complexity results in systems with higher reliability, which are easier to test and maintain.

The influence of cyclomatic complexity distribution on the understandability of xtUML models

Executable software models formalize functional software requirements. This means that the understandability of software models is of paramount importance. In this paper, we investigate the influence of cyclomatic complexity on the understandability of executable translatable unified modeling language (xtUML) models. We adapt traditional cyclomatic complexity metrics to different xtUML sub-models and present two different ways of measuring complexity distribution: horizontal, among elements of the same type, and vertical, among elements of different types. In order to test our hypothesis that cyclomatic complexity distribution influences the understandability of xtUML models, we have performed an experiment with student participants in which we have evaluated the understandability of three semantically equivalent xtUML models with different complexity distributions. Results indicate that a better distribution of cyclomatic complexity has a positive influence on model understandability.

Cyclomatic Complexity

Christof Ebert (Vector Consulting) and James Cain (Snell Advanced Media) discuss why the cyclomatic-complexity metric is popular in industry even though academia tends to shun it.

Empirical analysis of the relationship between CC and SLOC in a large corpus of Java methods and C functions

AbstractMeasuring the internal quality of source code is one of the traditional goals of making software development into an engineering discipline. Cyclomatic complexity (CC) is an often used source code quality metric, next to source lines of code (SLOC). However, the use of the CC metric is challenged by the repeated claim that CC is redundant with respect to SLOC because of strong linear correlation.We conducted an extensive literature study of the CC/SLOC correlation results. Next, we tested correlation on large Java (17.6 M methods) and C (6.3 M functions) corpora. Our results show that linear correlation between SLOC and CC is only moderate as a result of increasingly high variance. We further observe that aggregating CC and SLOC as well as performing a power transform improves the correlation.Our conclusion is that the observed linear correlation between CC and SLOC of Java methods or C functions is not strong enough to conclude that CC is redundant with SLOC. This conclusion contradicts earlier claims from literature but concurs with the widely accepted practice of measuring of CC next to SLOC. Copyright © 2015 John Wiley & Sons, Ltd.

LEXIMET: A Lexical Analyzer Generator including McCabe's Metrics.

Due to the complexity of designing a lexical analyzer for programming languages, this paper presents, LEXIMET, a lexical analyzer generator. This generator is designed for any programming language and involves a new feature of using McCabe's cyclomatic complexity metrics to measure the complexity of a program during the scanning operation to maintain the time and effort.

The Use of Cyclomatic Complexity Metrics in Programming Performance's Assessment

Programming course is usually difficult and complex as taken from a student's point of view. Complexity of a programming code may be determined by the number of linearly independent path of its source code. This paper will be discussing the consistencies of the complexity of the code supposedly produced by students based on the programming questions found in the programming assessment throughout a semester of studies. The comparisons of the level of complexity and the students’ grade for various assessments materials are done. Based on this a relationship is observed and the conclusion is drawn on whether the consistencies in producing a certain pattern of expected complexity of a programming code has an effect on students performance.

Predicting Software Projects Cost Estimation Based on Mining Historical Data

In this research, a hybrid cost estimation model is proposed to produce a realistic prediction model that takes into consideration software project, product, process, and environmental elements. A cost estimation dataset is built from a large number of open source projects. Those projects are divided into three domains: communication, finance, and game projects. Several data mining techniques are used to classify software projects in terms of their development complexity. Data mining techniques are also used to study association between different software attributes and their relation to cost estimation. Results showed that finance metrics are usually the most complex in terms of code size and some other complexity metrics. Results showed also that games applications have higher values of the SLOCmath, coupling, cyclomatic complexity, and MCDC metrics. Information gain is used in order to evaluate the ability of object-oriented metrics to predict software complexity. MCDC metric is shown to be the first metric in deciding a software project complexity. A software project effort equation is created based on clustering and based on all software projects’ attributes. According to the software metrics weights values developed in this project, we can notice that MCDC, LOC, and cyclomatic complexity of the traditional metrics are still the dominant metrics that affect our classification process, while number of children and depth of inheritance are the dominant from the object-oriented metrics as a second level.

An Empirical Study of Software Reuse in Reconstructive Maintenance

Software reuse has mainly been studied in software development. However, software reuse is also an important aspect in software maintenance. Reconstructive maintenance involves disassembling an old system into components and reassembling them into a new one. This paper discusses software reuse in reconstructive maintenance. An empirical study in an industrial software development environment is presented, involving a legacy system. In particular, the study has found that the cyclomatic complexity metric at some levels may be related to a function's reusability. The study also found that there seems to be a high correlation between the cyclomatic complexity metric of a function and the lines of code metric of the function. © 1997 by John Wiley & Sons, Ltd. J. Software Maintenance 9: 69–83, 1997.

Automatic programming assessment

Software metrics have been used extensively to provide quantitative measures of software characteristics. This paper aims at evaluating the relevance of using software metrics as means of assessing students' performance in programming. The study focusses on the use of four basic software metrics which are combined to form a single assessment score. The four metrics are respectively those which measure programming skill, complexity, programming style and programming efficiency. Measurements suggested that the lines of code metric is a good candidate for measuring programming skill. McCabe's cyclomatic complexity metrics have been adopted for measuring program complexity. Program execution times are used as the measuring yardsticks for programming efficiency. To facilitate automatic assessment, a program analyzer has been constructed which can provide measures of all the relevant software metrics together with the appropriate assessment scores. The tool was tested with sample assignments of Pascal programs and good distribution of marks has been obtained.

Cyclomatic complexity density and software maintenance productivity

A study of the relationship between the cyclomatic complexity metric (T. McCabe, 1976) and software maintenance productivity, given that a metric that measures complexity should prove to be a useful predictor of maintenance costs, is reported. The cyclomatic complexity metric is a measure of the maximum number of linearly independent circuits in a program control graph. The current research validates previously raised concerns about the metric on a new data set. However, a simple transformation of the metric is investigated whereby the cyclomatic complexity is divided by the size of the system in source statements. thereby determining a complexity density ratio. This complexity density ratio is demonstrated to be a useful predictor of software maintenance productivity on a small pilot sample of maintenance projects. >

The PEEK measurement program

A computer program called PEEK has been written to measure PL/I computer programs. The PEEK system makes three types of measurements of PL/I programs. PEEK measures (1) basic statistics, (2) cyclomatic complexity, and (3) software science metrics. Each of these types of measures gives a different insight into the nature of the particular computer program that is measured. The PEEK program has been developed as the central tool for doing a set of experiments in program measurement. Future versions of PEEK may be tailored as tools for developing and maintaining computer programs.

Evaluating and comparing software metrics in the software engineering laboratory

There has appeared in the literature a great number of metrics that attempt to measure the effort or complexity in developing and understanding software(1). There have also been several attempts to independently validate these measures on data from different organizations gathered by different people(2). These metrics have many purposes. They can be used to evaluate the software development process or the software product. They can be used to estimate the cost and quality of the product. They can also be used during development and evolution of the software to monitor the stability and quality of the product. Among the most popular metrics have been the software science metrics of Halstead, and the cyclomatic complexity metric of McCabe. One question is whether these metrics actually measure such things as effort and complexity. One measure of effort may be the time required to produce a product. One measure of complexity might be the number of errors made during the development of a product. A second question is how these metrics compare with standard size measures, such as the number of source lines or the number of executable statements, i.e., do they do a better job of predicting the effort or the number of errors? Lastly, how do these metrics relate to each other?