Complexity measures for object-oriented conceptual models of an application domain.

According to Norman Fenton few work has been done on measuring the complexity of the problems underlying software development. Nonetheless, it is believed that this attribute has a significant impact on software quality and development effort. A substantial portion of the underlying problems are captured in the conceptual model of the application domain. Based on previous work on conceptual modelling of aplication domains, the attribute 'complexity of a conceptual model' is formally defined in this papaer using elementary concepts from Measure Theory. Moreover, a number of complexity measures are defined and validated against this complexity definition. It is argued and demonstrated that these problem domain measures are part of a solution to the problem outlined by Norman Fenton.Model; Models;

Comparison of software complexity metrics in measuring the complexity of event sequences

One of the main challenges in software development is the complex structure of a system. The software development for event sequences is complex. It is a challenge to define a complexity metric for event sequences application. Lack of knowledge in complexity metric can lead to issues such as rises in software cost and delays in project timing. Numerous complexity metrics have been proposed and published, such as information flow complexity, lines of code, function points, and unique complexity metric. However, in the context of the event sequences, most of the research focuses on measuring web graphs, measuring the web traffic and how the complexity of the web impacts the customer. In this paper, the researchers studied and compared five different software complexity metrics. This paper describes the on-going research that addresses the issue to produce a unique weight to prioritise event sequences test cases

Measurement of Cognitive Functional Sizes of Software.

One of the major issues in software engineering is the measurement. Since traditional measurement theory has problem in defining empirical observations on software entities in terms of their measured quantities, Morasca tried to solve this problem by proposing Weak Measurement theory. Further, in calculating complexity of software, the emphasis is mostly given to the computational complexity, algorithm complexity, functional complexity, which basically estimates the time, efforts, computability and efficiency. On the other hand, © 2013, 22 pp. understandability and compressibility of the software which involves the human interaction are neglected in existing complexity measures. Recently, cognitive complexity (CC) to calculate the architectural and operational complexity of software was proposed to fill this gap. In this paper, we evaluated CC against the principle of weak measurement theory. We find that, the approach for measuring CC is more realistic and practical in comparison to existing approaches and satisfies most of the parameters required from measurement theory.One of the major issues in software engineering is the measurement. Since traditional measurement theory has problem in defining empirical observations on software entities in terms of their measured quantities, Morasca tried to solve this problem by proposing Weak Measurement theory. Further, in calculating complexity of software, the emphasis is mostly given to the computational complexity, algorithm complexity, functional complexity, which basically estimates the time, efforts, computability and efficiency. On the other hand, © 2013, 22 pp. understandability and compressibility of the software which involves the human interaction are neglected in existing complexity measures. Recently, cognitive complexity (CC) to calculate the architectural and operational complexity of software was proposed to fill this gap. In this paper, we evaluated CC against the principle of weak measurement theory. We find that, the approach for measuring CC is more realistic and practical in comparison to existing approaches and satisfies most of the parameters required from measurement theory

A Suite of Object Oriented Cognitive Complexity Metrics

Object orientation has gained a wide adoption in the software development community. To this end, different metrics that can be utilized in measuring and improving the quality of object-oriented (OO) software have been proposed, by providing insight into the maintainability and reliability of the system. Some of these software metrics are based on cognitive weight and are referred to as cognitive complexity metrics. It is our objective in this paper to present a suite of cognitive complexity metrics that can be used to evaluate OO software projects. The present suite of metrics includes method complexity, message complexity, attribute complexity, weighted class complexity, and code complexity. The metrics suite was evaluated theoretically using measurement theory and Weyuker’s properties, practically using Kaner’s framework and empirically using thirty projects

SIMPLIFIED READABILITY METRICS

This paper describes a new approach to measuring the complexity of software systems with considering their readability. Readability Metrics were first proposed by Chung and Yung 181 in 1990. Software industry uses software metrics to measure the complexity of software systems for software cost estimation, software development control, software assurance, software testing, and software maintenance [3], [71, [9], 151, [18]. Most of the software metrics measure the software complexity by one or more of the software attributes. We usually class@ the software attributes that software metrics use for measuring complexity into three categories: size, control flow, and data flow [5], f71. All the three categories concern with the physical activities of software development. Readability Metrics have been outstanding among the existing software complexity metrics for taking nonphysical software attributes, like readability, into considerations [8]. The applications of Readability Metrics are good in indicating the additional efforts required for less readable software systems, and help in keeping the software systems maintainable. However, the numerous metrics and the complicated formulas in the family usually make it tedious to apply Readability Metrics to large scale software systems. In this paper, we propose a simplified approach to Readability Metrics. We reduce the number of required measures and keep the considerations on software readability. We introduce our Readability model in a more formal way. The Readability Metrics preprocesses algorithm is developed with compilers front-end techniques. The experiment results show that this simplified approach has good predictive power in measuring software complexity with software readability, in addition to its ease of applying. The applications of Readability Metrics indicate the readability of software systems and help in keeping the source code readable and maintainable.Information Systems Working Papers Serie

Complexity metrics for measuring the understandability and maintainability of Business Process Models using Goal-Question-Metric (GQM)

Business Process Models (BPMs), often modeling language such as UML activity between the created using stakeholders in the can provide us a diagrams, Event- Driven Process Chains Markup Language (EPML) and Yet Another Workflow Language (YAWL), serve as a base for communication that adequate software development process. In order to fulfill this purpose, they should be easy to understand and easy to maintain. For this reason, it is useful to have measures information about understandability and maintainability of the BPM. Although there are hundreds of software complexity measures that have been described and published by many researchers over the last few decades, measuring the complexity of business process models is a rather new area of research with only a small number of contributions. In this paper, we provide a comprehensive report on how existing complexity metrics of software were adapted in order to analyze the current business process models complexity. We also proposed a Goal- Question-Metric (GQM) framework for measuring the understandability and maintainability of BPMs

Measuring the Overall Complexity of Graphical and Textual IEC 61131-3 Control Software

Software implements a significant proportion of functionality in factory automation. Thus, efficient development and the reuse of software parts, so-called units, enhance competitiveness. Thereby, complex control software units are more difficult to understand, leading to increased development, testing and maintenance costs. However, measuring complexity is challenging due to many different, subjective views on the topic. This paper compares different complexity definitions from literature and considers with a qualitative questionnaire study the complexity perception of domain experts, who confirm the importance of objective measures to compare complexity. The paper proposes a set of metrics that measure various classes of software complexity to identify the most complex software units as a prerequisite for refactoring. The metrics include complexity caused by size, data structure, control flow, information flow and lexical structure. Unlike most literature approaches, the metrics are compliant with graphical and textual languages from the IEC 61131-3 standard. Further, a concept for interpreting the metric results is presented. A comprehensive evaluation with industrial software from two German plant manufacturers validates the metrics' suitability to measure complexity.Comment: 8 pages, https://ieeexplore.ieee.org/abstract/document/9444196