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Background Network Properties In-degree and out-degree distributions observe power law → scale-free networks What We Did Our approach: • Model software as a directed graph • Apply dynamic network analysis. Dynamic Network Analysis Strategy: 1. Study network properties • Size • Degree distribution of vertices • Communities of vertices 2. Study vertex properties • Degree – fan-in and fan-out of vertex • Betweenness Centrality (BC) – ratio of shortest paths through a vertex • Clustering Coefficient (CC) – connections between neighbors • Articulation Point (AP) – removal causes network disconnection 3. Compute disruption measures 4. Characterize changes in vertex properties 5. Characterize new vertices 6. Compare against project data Real-world software systems have large numbers of components (e.g., classes, functions, etc.) It is difficult to get a quick summary of how system evolved after a major change, such as perfective maintenance activity or new software release. Key Idea: Measure how the relationships are affected or disrupted by such changes. Data: JHotDraw 5 Vertex Properties Disruption Important Vertices Measuring Disruption from Software Evolution Activities Using Graph-Based Metrics Prashant Paymal, Rajvardhan Patil, Sanjukta Bhowmick, Harvey Siy, University of Nebraska at Omaha Ver Date Last Change V1 3/9/2001 Merge to JHotDraw 5.2 (using JFC/Swing GUI components) V2 10/24/2001 before merge for version 5.3 (dnd, undo,...), merge dnd (before 5.3) V3 8/4/2002 after various merges... (before 5.4 release) V4 11/8/2002 Refactor to use StandardStorageFormat as a superclass. V5 5/8/2003 Refactoring of Cursor: - java.awt.Cursor (class) has been sistematically replaced V6 1/9/2004 After renaming the CH.ifa.draw to org.jhotdraw Disruption – extent to which class relationships are disrupted in the course of software evolution. 1. Value disruption – measures change in the values of vertex properties. 2. Rank disruption – measures change in ordering of vertex properties. Communities of Vertices Jump in size from V2-V3 and V4-V5. Positive correlation Highly connected classes are central Negative in V1, V2 Positive in V4,V5 Negative in V1, V2 Positive in V4,V5 Find closely connected groups of vertices (communities). Network of V1 Node size: CC Blues: BC ≤ 10 Greens: 10 < BC ≤ 100 Yellows: BC > 100 Earlier versions: New vertices are clusters of interdependent classes. Latter versions: New vertices have few neighboring vertices. New vertices are added to network periphery. Distribution of important vertices decreases over time due to addition of new (and less important) vertices. Higher values from V2-V3 and V4-V5 “High importance” – high degrees, BC, or CC, or is AP. Vertices that were important in V1 tend to stay important. Large communities have more relationships within than to external vertices → modular Average count of edges per vertex appears capped at 3-4 edges → manageable communities Effect on Quality Higher percentage of bug fix changes relative to total changes after V3 and V5, coinciding with periods of higher disruption. JHotDraw Findings Acknowledgements This research was funded in part by a Nebraska EPSCoR First Award grant and by the College of IS&T, University of Nebraska at Omaha. • Higher disruption from V2-V3 and V4-V5 • Even so, core design appears stable. • Newer vertices added to network periphery. • Important nodes stay important. • Similar communities across versions. • Degree of disruption may help explain why bug incidence increases (future work) “Figure” “DrawingView”
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