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Simon: Modeling and Analysis of Design Space Structures

Department of. C omputer Science. School of Engineering. and Applied Science. University of Virginia. Simon: Modeling and Analysis of Design Space Structures. Yuanfang Cai, Kevin Sullivan , University of Virginia, {yc7a, sullivan}@cs.virginia.edu.

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Simon: Modeling and Analysis of Design Space Structures

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  1. Department of Computer Science School of Engineering and Applied Science University of Virginia Simon: Modeling and Analysis of Design Space Structures Yuanfang Cai, Kevin Sullivan, University of Virginia, {yc7a, sullivan}@cs.virginia.edu The structure of the coupling relation on design decisions is a key factor influencing the evolvability properties and the economic value of a design. However, we lack the basic science and tools to rigorously address the fundamental problem of reasoning the economic implications of design coupling structures. The work of Baldwin and Clark is an important step toward a theory of the relationship between structure and value. In order to enable rigorous validation and perhaps the eventual use of their ideas for software engineering, we formalize their ideas by formally modeling software designs using Augmented Constraint Networks (ACNs). We have been developing a set of analysis methods based on the formalization, including the automatic derivation of Design Structure Matrices (DSMs) and quantifiable Design Impact Analysis (DIA). Simon is our prototype tool supporting ACN modeling and DSM/DIA analysis. Design Modeling: Augmented Constraint Network 1. Constraint Network 2. Cannot Influence Relation 3. Multiple Clustering View 2: Task Assignment View 1: Random Variables: Design dimensions or environment factors Can influence Cannot influence View 3: Design Rule Domain: Design choices or environment conditions Some design decisions dominate other decisions; Environment factors are often out of designer’s control Constraint: Relations among design decision or environment conditions One design can be viewed from multiple perspectives Design Analysis (1) Design Structure Matrix (2) Design Impact Analysis Evolution Paths Changed Design Decisions Automatically Derived DSM with Rigorous Semantics New Design 1. Given a design 2. Change some decisions 3. Review the consequences All the variables that depend on the given variable • Results • Automatically generated DSMs are consistent with the DSMs we published previously [sullivan01], but reveal subtle errors in the manual versions • Automate Parnas’s qualitative analysis of KWIC with quantifiable results. All the variables on which the given variable depends

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