Susan Ferreira

Understanding the effects of requirements volatility in software engineering by using analytical modeling and software process simulation

This paper introduces an executable system dynamics simulation model developed to help project managers comprehend the complex impacts related to requirements volatility on a software development project. The simulator extends previous research and adds research results from an empirical survey, including over 50 new parameters derived from the associated survey data, to a base model. The paper discusses detailed results from two cases that show significant cost, schedule, and quality impacts as a result of requirements volatility. The simulator can be used as an effective tool to demonstrate the complex set of factor relationships and effects related to requirements volatility.

A System Dynamics Perspective of Patient Satisfaction in Healthcare

Abstract Healthcare systems face challenges including diminishing resources and increasing demands. The challenges need to be balanced in this complex system of systems to ensure a sustainable quality of life. Sustainability considers the needs of future generations without compromising the needs of current generations. The social component of sustainability is one of the important areas in healthcare sustainability. The social component focuses on considerations such as equity, empowerment, accessibility, participation, cultural identity, and institutional stability. Patient satisfaction is a key factor in the social element. Patient satisfaction represents patient fulfillment in regards to the cost, accessibility to services and resources, and patient wellbeing. It is analogous to ‘customer satisfaction’. A systems thinking approach is applied to analyze the social aspect in healthcare systems. This paper explores important factors and factor relationships in healthcare social sustainability related to patient satisfaction using a system dynamics approach.

Behavioral characterization: Finding and using the influential factors in software process simulation models

Abstract Most software process simulation work has focused on the roles and uses of software process simulators, on the scope of models, and on simulation approaches. Consequently, the literature reflects a growing body of models that have recently been characterized by modeling purpose, scope, key result variables, and simulation method. While the software process simulation arena is maturing, little effort appears to have been given to statistical evaluation of model behavior through sensitivity analysis. Rather, most of software process simulation experimentation has examined selected factors for the sake of understanding their effects with regard to particular issues, such as the economics of quality assurance or the impact of inspections practice. In a broad sense, sensitivity analysis assesses the effect of each input on model outputs. Here, we discuss its use for behaviorally characterizing software process simulators. This paper discusses the benefits of using sensitivity analysis to characterize model behavior; the use of experimental design for this purpose; our procedure for using designed experiments to analyze deterministic simulation models; the application of this procedure to four published software process simulators; the results of our analysis; and the merits of this approach.

Reducing the risk of requirements volatility: findings from an empirical survey

Requirements volatility is a common development project risk that can have severe impacts. An empirical survey of over 300 software project managers and other software development personnel was performed to examine effects of various software development factors on requirements volatility. This paper reports the survey data results showing relationships between a number of software development process factors and requirements volatility. Key software project factors studied for their relationship with requirements volatility include process maturity level and various process techniques used for requirements engineering activities, such as requirements elicitation, prototyping, analysis and modeling, specification, and reviews. Significant correlations between the process factors and requirements volatility resulted from the analysis of some of the factors. The use of particular requirements engineering process techniques showed correlations with lower levels of requirements volatility. Other findings indicated that projects which used some types of prototypes to elicit requirements had higher levels of requirements volatility in later phases of the development cycle than lower levels, as one might expect. The presented results can be used by software development managers to proactively address and possibly mitigate the risk of requirements volatility, and to understand the potential for increased requirements volatility when certain methods are utilized. Copyright

Applying systems thinking to assess sustainability in healthcare system of systems

Healthcare systems face increasing demands and reduced resources. Therefore, there is growing attention paid to sustainability in healthcare. Healthcare is a complex system of systems. This paper discusses healthcare system challenges and the need to consider a sustainable approach in addressing these challenges. An equitable and balanced approach is required to deal with the demands related to healthcare sustainability including societal needs, financial constraints, and intensifying environmental expectations. System dynamics, a systems thinking approach, is applied to help address the challenges of complexity, variability, and uncertainty related to a sustainable model for healthcare. Causal models illustrating the system dynamics factors and relationships are presented. Analysis of the system dynamics and associated factors produced a notional set of healthcare sustainability indicators. These indicators provide important measures of healthcare sustainability.

Developing Systems Engineering Ontologies

Systems engineering ontologies are required to assist interested parties in understanding the systems engineering disciplines broad and multi-faceted nature. This paper discusses the need for and general benefits of an ontology. The authors discuss the use of the domain knowledge acquisition process ontology modeling technique and its application to capture a systems engineering functional domain ontology. A preliminary systems engineering functional domain ontology generated using the ontology development methodology is presented. This systems engineering ontology will continue to be refined over time. This paper discusses plans to develop a system of systems engineering ontology using the developed systems engineering ontology.

Constructing a general framework for systems engineering strategy

The strategic application of systems engineering can lead to sustained competitive advantage for an organization. This paper expands the scope of the systems engineering strategy dialogue from its existing focus which is primarily based on specific case studies to a broader context and introduces a systems engineering strategy framework. The framework defines a state-based model for systems engineering strategy. States are grouped by organization, environment, product or service, and systems engineering process characteristics. These characteristics provide an extensive set of considerations for making a strategic decision related to systems engineering. This paper discusses using the framework to support a variety of systems engineering decisions, including decisions related to investment and risk analysis. Decision makers can apply the framework to make more informed choices that ultimately will lead to an organizations long-term survival.

Advancing systems engineering in support of the bid and proposal process

Qualifying strategic opportunities to pursue and capitalizing on those opportunities is key to the very survival of an organization. Successful organizations that thrive on contract work capture contracts and earn a profit executing those contracts. Systems engineers are generally the “cradle to grave” technical points of contact for projects and are charged with guiding the definition of the technical solutions in the early lifecycle stages. This paper examines existing research that may guide systems engineering in support of bid and proposal pursuits. Systems engineering, software engineering, bid management, economics, finance, game theory, and cognitive psychology literature are all mined for areas related to bidding. As a result, a number of research opportunities are identified.

Applying Systems Thinking to Analyze Wind Energy Sustainability

Abstract Wind energy, along with other renewable energy sources, has become an alternative to traditional energy sources to meet the growing energy demand. Wind energy is considered to be one of the cleanest sources of energy. Wind energy sustainability focuses on a balance between economic, social and environmental objectives. However, wind energy faces various challenges associated to sustainability. This paper presents a way to analyze wind energy sustainability using a systems thinking approach.

A Methodology for Design Ontology Modeling

Recent research has focused on the use of ontologies to promote the sharing of knowledge. Ontologies are becoming increasingly important because they provide the critical semantic foundation for the rapidly expanding field of knowledge. They are very useful for knowledge reuse, knowledge sharing, and enterprise modeling. A design ontology is a hierarchically structured set of terms for describing a design domain that can be used as a skeletal foundation for a knowledge base. It can help the collaborative design team by providing accurate design information and guidelines. This research develops a methodology called domain knowledge acquisition process (DKAP) for creating an ontology of product and process design using IDEF5 and generates a consistency matrix for checking the accuracy of captured information. DKAP is a step-by-step methodology, which captures the product & process design knowledge, stores this knowledge in a reusable format, and shares this knowledge across enterprises. DKAP addresses three critical aspects of a design ontology. It explores the availability of similar domain ontologies for reuse, checks the accuracy & consistency of captured knowledge, and allows the sharing of the captured knowledge.