Estimating Design Complexity

IEEE Transactions on Engineering Management, 2020

Modern engineering systems are increasing in complexity and demanding higher levels of performance, safety, durability, and quality. This has led to an increase in the number of functional requirements and system elements. Subsequently, meeting the target performances and schedules requires more design effort and cost. To ensure on-time development and delivery of the final system, engineers need to allocate the right amount of available resources to design and develop various subsystems. However, estimating the efforts required to design and develop a system and its constituents during the early design stage is significantly challenging. Often, initial estimations are made by subject matter experts who base their judgments on the perceived difficulty of designing the subsystems. In this article, to quantify the perceived design difficulty, a new metric based on the degree of relationship between the system requirements and elements is proposed. This metric utilizes the domain mapping matrix and matrix energy to quantify the complexity of system design. Using the proposed metric, the relationship between system design complexity and design difficulty perceived by engineering experts is established and demonstrated through a case study involving a column-type electric power steering system.

2003

Developing objective measures for evaluating and measuring the complexity of design would facilitate (1) empirical studies that require the use of equivalent but different design problems, (2) the development of design curriculums, and (3) the comparison of computer aided design automation tools. This paper surveys and evaluates different approaches to defining complexity in design for the design problem, process, and product. Three fundamental aspects to complexity are identified, size, coupling, and solvability, and expanded with respect to the three elements of design, problem, process, and product. Alternative methods for measuring these characteristics of the design are based on computational, information, and traditional design views of complexity. A method of measuring size as it relates to complexity is proposed for measuring the information content of design. A second method is proposed for decomposing a graph-based representation of design that provides a measure of the interconnectedness as it relates to complexity. Finally, two methods are proposed for determining the solvability complexity of design based on the effort involved and the degree of freedom of design. These measures are developed specifically for parametric and geometric problems as found in the embodiment design, but these principles may be applied beyond this.

International Journal of Hybrid Information Technology, 2014

This paper presents a new complexity metric for Object-Oriented (OO) design measurement to find at the design stage whether the classes become more complex, Moderate complex or less complex. The proposed metric is theoretically evaluated against the Weyuker's properties as well as empirically evaluated against three open source software system. Furthermore, for validating the validity of new complexity metric, the paper has also presented the comparison of proposed metric with some well known complexity metrics like Weighted Method per Class (WMC) metric of Chidamber and Kemerer (CK), Class Complexity (CC) metric of Balasubramanian, and Complexity Metric for OO Design (CMOOD) metric of Rajnish and Bhattacherjee (RB) against the same three open source software system..Automated tool were used to generate the metric values and for analyzing the results, IBM SPSS software used. The results in this paper indicates that the new complexity metric is correlated well with existing complexity metrics and may be used as predictors of complexity of class.

2010

Developing objective measures for evaluating and measuring the complexity of design would facilitate (1) empirical studies that require the use of equivalent but different design problems, (2) the development of design curriculums, and (3) the comparison of computer aided design automation tools. This paper surveys and evaluates different approaches to defining complexity in design for the design problem, process, and product. Three fundamental aspects to complexity are identified, size, coupling, and solvability, and expanded with respect to the three elements of design, problem, process, and product. Alternative methods for measuring these characteristics of the design are based on computational, information, and traditional design views of complexity. A method of measuring size as it relates to complexity is proposed for measuring the information content of design. A second method is proposed for decomposing a graph-based representation of design that provides a measure of the interconnectedness as it relates to complexity. Finally, two methods are proposed for determining the solvability complexity of design based on the effort involved and the degree of freedom of design. These measures are developed specifically for parametric and geometric problems as found in the embodiment design, but these principles may be applied beyond this.

Journal of Systems Architecture, 2003

The increasing complexity of processing algorithms has led to the need of more and more intensive specification and validation by means of software implementations. As the complexity grows, the intuitive understanding of the specific processing needs becomes harder and harder. Hence, the architectural implementation choices or the choices between different possible software/hardware partitioning become extremely difficult tasks. Moreover, it is also desirable to understand and measure the algorithm complexity at the highest possible level near to the algorithmic level so as to be able to take the more appropriate actions. Automatic tools to perform such analysis become nowadays a fundamental need.

When measuring design complexity (DC) in the field of medical devices, some of the aspects to consider are the number of components, interactions between components, part variations and the product's overall functionality. Taking into consideration these aspects, several authors have developed metrics in an effort to quantitatively measure DC. While these metrics consider DC from different perspectives, there is an underlying relationship between each one. This paper presents a study of seventy-five knee replacement devices in an effort to understand the connection between these factors and complexity. To achieve this, product information and surgical techniques were gathered to analyze the devices' complexity using six DC metrics. Furthermore, a correlation analysis was performed to unravel the relationships between these metrics for knee replacements and in contrast to previous results with hip replacements. This research provides a framework for future developments with an understanding of complexity metrics in the field of medical devices.

Design Studies, 2001

2008

It is widely accepted that sizing or predicting the volumes of various kinds of software deliverable items is one of the first and most dominant aspects of software cost estimating. Most of the cost estimation model or techniques usually assume that software size or structural complexity is the integral factor that influences software development effort. Although sizing and complexity measure

System design complexity is growing rapidly. As a result, current development costs are constantly increasing. It is becoming increasingly difficult to estimate how much time it will take to design and verify these designs, which are getting denser and increasingly more complex. To compound this problem, circuit design cost estimation still does not have a quantitative approach. Although designing a system is very resource consuming, there is little work invested in measuring, understanding, and estimating the effort required. To address part of the current shortcomings, this paper introduces µPCBComplexity, a methodology to measure and estimate PCB (printed circuit board) design effort. PCBs are the central component of many systems and require large amounts of resources to properly design and verify. µPCBComplexity consists of two main parts; a procedure to account for the contributions of the different elements in the design, and a non-linear statistical regression of experimental measures in order to determine a good design effort metric. We use µPCBComplexity to evaluate a series of design effort estimators for twelve PCB designs. By using the proposed µPCBComplexity metric, designers can estimate PCB design effort.