The first part of this paper outlined the Statistical Agent-based Model of Development and Evaluation (SAbMDE) and demonstrated the model’s ability to estimate development cycle resource utilization. This second part of the paper explores the model’s ability to compute development cycle information content and process risk. Risk managers focus mostly on outcome risk, i.e., the likelihood that a running system will behave in an undesirable manner. SAbMDE assumes that a subset of outcome risks are not inherent and immutable but are, instead, the result of defects and vulnerabilities introduced during the system’s development process. The likelihood of defect and vulnerability introduction is a process risk. SAbMDE further assumes that measuring process risk is a prerequisite for minimizing defects and vulnerabilities and, therefore, outcome risk. The model implements the measurement with Shannon’s information–probability relationship similar to its use in Axiomatic Design Theory (ADT). This paper details the SAbMDE’s information and risk calculations and demonstrates those calculations with examples. The process risk calculation is consistent with and offers a mechanism for the ADT Information Axiom.

This paper presents results produced by a domain-independent system development model that enables objective and quantitative calculation of certain development cycle characteristics. The presentation recounts the model’s motivation and includes an outline of the model’s structure. The outline shows that the model is constructive. As such, it provides an explanatory mechanism for the results that it produces, not just a representation of qualitative observations or measured data. The model is a Statistical Agent-based Model of Development and Evaluation (SAbMDE); and it appears to be novel with respect to previous design theory and methodology work. This paper focuses on one development cycle characteristic: resource utilization. The model’s resource estimation capability is compared to Boehm’s long-used software development estimation techniques. His Cone of Uncertainty (COU) captures project estimation accuracy empirically at project start but intuitively over a project’s duration. SAbMDE calculates estimation accuracy at start up and over project duration; and SAbMDE duplicates the COU’s empirical values. Additionally, SAbMDE produces results very similar to the Constructive Cost Model (COCOMO) effort estimation for a wide range of input values.

This paper argues that development (product, system, software, etc.) is an inherently transdisciplinary activity. Development is defined as the conversion of ideas into their manifestations. This conversion is often characterized by development phases, e.g., concept, requirements, design, implementation, and evaluation/testing (CRDIE). Iterative sequences of these phases form development cycles. Development cycles drive new product creation as well as product quality and cost and utility. Consequently, understanding development cycles is important. Models can provide insight; however, end-to-end quantitative development cycle models are, at best, rare. This paper outlines such a model, the Statistical Agent-based Model of Development and Evaluation (SAbMDE). For purposes of this paper, transdisciplinarity is defined as a developer’s holistic view of reality as filtered by that developer’s sensory input and perception of that reality. The model builds its mathematical and logical structures on a foundational concept that includes and describes this sensory and perceptual integration. Because the proposed model has this transdisciplinary characteristic, the model's use and results will have transdisciplinary implications. One implication: Ideas are discovered, not created. Another: A developer must first adjust their perception to see the development path that leads to a desired end product before they can traverse that path. A third: The ordering of information in a development space must be maintained.. This paper defines a minimal SAbMDE model that logically and mathematically reveals these and other SAbMDE transdisciplinarity implications.

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