Software Process simulation

Abstract: Session 1 The Empirical Paradigm.- The Empirical Paradigm Introduction .- Approaches for Empirical Validation.- Techniques for Empirical Validation.- Status of Empirical Research in Software Engineering.- Position Papers.- Aggregation of Empirical Evidence.- Empirical Evaluation in Software Engineering: Role, Strategy, and Limitations.- New Opportunities for Empirical Research.- Empirical Paradigm: Position Paper.- The Value of Empirical Evidence for Practitioners and Researchers.- Exploration Versus Confirmation.- Empirical Paradigm - The Role of Experiments.- The Role of Controlled Experiments in Software Engineering Research.- Position Papers.- Creating Real Value in Software Engineering Experiments.- From Silver Bullets to Philosophers' Stones: Who Wants to Be Just an Empiricist?.- Social and Human Aspects of Software Engineering.- Longitudinal Studies in Evidence-Based Software Engineering.- The Use of Grounded Theory in Empirical Software Engineering.- Historical Review.- Exploration and Confirmation: An Historical Perspective.- Working Group Results.- Combining Study Designs and Techniques Working Group Results.- Optimizing Return-On-Investment (ROI) for Empirical Software Engineering Studies Working Group Results.- The Role of Controlled Experiments Working Group Results.- Discussion and Summary.- The Empirical Paradigm Discussion and Summary.- Session 2 Measurement and Model Building.- Measurement and Model Building Introduction.- Data Sharing.- Data Collection, Analysis, and Sharing Strategies for Enabling Software Measurement and Model Building.- Knowledge Acquisition in Software Engineering Requires Sharing of Data and Artifacts.- Effective Data Interpretation.- Effective Data Interpretation.- Software Support Tools and Experimental Work.- Position Papers.- Measurement and Interpretation of Productivity and Functional Correctness.- Synthesising Research Results.- On the Quality of Data.- Working Group Results.- Potential of Open Source Systems as Project Repositories for Empirical Studies Working Group Results.- Data Sharing Enabling Technologies Working Group Results.- Documenting Theories Working Group Results.- Discussion and Summary.- Measurement and Model Building Discussion and Summary.- Session 3 Technology Transfer and Education.- Technology Transfer and Education Introduction.- Technology Transfer.- Empirical Studies as a Basis for Technology Transfer.- Position Papers.- Relationships and Responsibilities of Software Experimentation.- The (Practical) Importance of SE Experiments.- How to Improve the Use of Controlled Experiments as a Means for Early Technology Transfer.- Extending Empirical Studies to Cover More Realistic Industrial Development and Project Management Issues.- Empirical Case Studies in Industry: Some Thoughts.- Software Process Simulation Frameworks in Support of Packaging and Transferring Empirical Evidence.- Structuring Families of Industrial Case Studies.- Education.- Empirical Software Engineering: Teaching Methods and Conducting Studies.- Educational Objectives for Empirical Methods.- Position Papers.- On "Landscaping" and Influence of Empirical Studies.- Involving Industry Professionals in Empirical Studies with Students.- Working Group Results.- Industry-Research Collaboration Working Group Results .- Teaching Empirical Methods to Undergraduate Students Working Group Results.- Discussion and Summary.- Technology Transfer and Education Discussion and Summary.- Roadmapping.- Empirical Software Engineering Research Roadmap Introduction.- Working Group Results.- Roadmapping Working Group 1 Results.- Roadmapping Working Group 2 Results.- Roadmapping Working Group 3 Results.- Roadmapping Working Group 4 Results.- Discussion and Summary.- Empirical Software Engineering Research Roadmap Discussion and Summary.

Abstract: Software process models have been simulated using system dynamics and discrete modeling paradigms. System dynamics models describe the interaction between project factors, but do not easily represent queues and discrete process steps. On the other hand, discrete event models describe process steps, but may not have enough events to represent feedback loops accurately. We develop a combined model that represents the software development process as a series of discrete process steps executed in a continuously varying project environment. We demonstrate the feasibility of this model by combining a discrete event model of the ISPW6 software process example with the system dynamics model developed by Abdel-Hamid and Madnick. The combination of these two modeling paradigms creates the opportunity to examine new problems and issues that are highly relevant to software project managers. Copyright © 2000 John Wiley & Sons Ltd