Is There an Underlying Theory


of Software Project Management?



By Glen B. Alleman, MSSM


AnnMarie Oien, Ph.D.


Abstract: Traditional project management methods are based on scientific principles considered “normal science” but lack a theoretical basis for this approach. [35, 36, 69] These principles result in linear stepwise refinement of the project’s outcomes by applying the planning-as-management paradigm. Linear feedback methods adjust plans made in this paradigm. These plans cannot cope with the multiple interacting and continuously changing technology and market forces. They behave as a linear, deterministic, Closed-Loop control system.

A Closed-Loop adaptive control paradigm parallels this approach and agile project management methods. From these, a comparison is made between project management practices and the tenets of agile development processes in terms of feedback control and emergent solutions with a control system capable of adjusting to the changes brought about by changes in the dynamics of the process, disturbances, or some other cause not established in the original plan.

This paper suggests that when managing in the presence of uncertainties that create a risk to project success, adaptive control theory may be better suited as a model for project management in a rapidly changing, dynamically evolving network of statistical processes than traditional linear approaches.


Because large-scale software projects increasingly affect the public good, the standard science paradigm is insufficient to model their complexity and potential consequences. The post-normal science paradigm offers a better fit, using a robust management approach predicated on a risk-taking ethic. [13]

Project success is a frequent topic in project management, but it needs to be better understood in how to reach that success. [770] Since the early days of the software industry, managing software development projects has been fraught with risk to project success created by uncertainty. [1] While the technical content of products and the methods used to build those products has changed over time, the fundamental issues determining a project’s success or failure have remained constant.

Traditional program control systems must be better suited to respond to changes encountered in software development projects. In the development software intensive systems, research shows there are four primary root causes of project failure:

Unrealistic performance expectations,

Unrealistic cost and schedule estimates based on inadequate risk-adjusted growth models,

Inadequate assessment of risk and unmitigated exposure to these risks without proper handling plans,

Unanticipated technical issues without alternative plans and solutions to maintain the effectiveness of the project’s planned progress.

In the presence of these conditions, the success rate of software development could have been better when applying traditional methods in complex development software development project environments. [330] The conventional, linear, stepwise approach to software development has its roots in the project management methods of the 1970s. It was clear then, and has become clear today, that this approach to managing projects is inappropriate in many domains. [66, 67] The project management literature needs to include an answer to the question – is there an underlying theory of project management appropriate for complex, Software Intensive Systems development projects? [53], [77]

A secondary question is – can a theory be constructed consistent with adaptive feedforward control systems and agile development processes currently in use in manufacturing, science, engineering, economics, biology, and ecology?

This paper describes an approach to applying theories in other domains that match the behavioral aspects of software project management. The theory of Closed Loop Adaptive Control Systems is one choice.  Performance references, control loops, and stochastic processes have similar paradigms in dynamics systems and project management. In addition, the theory of complex adaptive systems and adaptive controls for those systems have a similar paradigm in “agile” software development.


To read entire paper, click here

How to cite this paper: Alleman, G. B., and Oien, A. (2023). Is There an Underlying Theory of Software Project Management? PM World Journal, Vol. XII, Issue V, May. Available online at https://pmworldlibrary.net/wp-content/uploads/2023/05/pmwj129-May2023-Alleman-Oien-underlying-theory-of-software-pm-2.pdf

About the Authors

Glen B. Alleman

Niwot Ridge, LLC


Glen B. Alleman leads the Program Planning and Controls practice for Niwot Ridge, LLC. In this position, Glen brings his 30+ years’ experience in program management, systems engineering, software development, and general management to bear on problems of performance-based program management.  Mr. Alleman’s experience ranges from real time process control systems to product development management and Program Management in a variety of firms including Logicon, TRW, CH2M Hill, SM&A, and several consulting firms before joining Niwot Ridge, LLC. Mr. Alleman’s teaching experience includes university level courses in mathematics, physics, and computer science. Glen can be contacted at galleman@niwotridge.com


AnnMarie Olien, PhD

Colorado, USA


 AnnMarie Oien, retired Technical Fellow of L3Harris Technologies, graduated with a PhD in Atomic and Laser Physics in 1996 and spent the next 25 years gaining expertise in Solid State and Nonlinear Laser Physics, Root Cause Analysis, Earned Value Management, Program Management, Agile Software Development, Systems Thinking, Data Analytics, and Senior Executive Strategic Planning facilitation as a Master Black Belt in Lean Six Sigma Process Improvement.  She has enjoyed codifying her knowledge into internal guidance publications for both Lockheed Martin Space and L3 Harris Technologies as well as publishing 20+ papers in external refereed conferences and journals in multiple fields.   AnnMarie can be contacted at annmarie.oien@comcast.net.

[1]   Poor management practices are one source of project failure. This paper does not address these management processes, but instead addresses the failure modes from uncertainties that create risk encountered on the project. Poor management is a risk, but research shows that unaddressed reducible (Epistemic) and irreducible (Aleatory) uncertainties are the primary source of project failure once management processes have been addressed.