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Coherent Dependence Clusters

Coherent Dependence Clusters. Syed Islam. Agenda. Coherent Clusters (Specialized Dependence Cluster). Visualization Tool for Dependence Clusters . Mapping Source Code Constructs to Clusters. Overlapping Clusters. Program Comprehension / Maintenance Fault Prediction.

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Coherent Dependence Clusters

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  1. Coherent Dependence Clusters Syed Islam

  2. Agenda • Coherent Clusters (Specialized Dependence Cluster) • Visualization Tool for Dependence Clusters • Mapping Source Code Constructs to Clusters • Overlapping Clusters • Program Comprehension / Maintenance • Fault Prediction

  3. Dependence Cluster • A dependence cluster is a maximal set of program statements where each statement depends on all other. • Minimal Slice

  4. Dependence Cluster (Slice-Based) • A Slice-Based Dependence Clusters is a maximal set of program statements all of which are in each others slice. • Two statementsthatmutually depend upon each other must be in each others slice. i j

  5. Dependence Cluster .. • Remembering all the slices is too expensive. • Approximation techniques were used • Same SliceCluster • Same Slice-Size Cluster.

  6. MSG -Monotone Slice-Size Graph • Calculate the Slice Sizes for each SDG vertex of a program. • Plot all slice sizes of the program in one graph in monotonically increasing order of the sizes. X axis: percentage of slices represented Y axis: normalised slice size

  7. Coherent Clusters • Same-Slice Dependence cluster have internal and external requirements. • Statements of Clusters constructed from Backward Slices areall influenced by the same set of statements. (Backward-Slice Cluster) • Statements of Clusters constructed from Forward Slices all influence the same set of statements. (Forward-Slice Cluster). • A Coherent Cluster is a set of statements where each statement has the same backward slice and the same forward slice.

  8. Dependence Cluster • Forward-Slice Cluster • Backward-Slice Cluster • Coherent Cluster What forms a cluster? x 1 2 5 3 4 y

  9. SCG – Slice/Cluster-Size Graph

  10. SCG – Slice/Cluster-Size Graph B-MSG F-MSG

  11. SCG – Slice/Cluster-Size Graph B-MCG F-MCG

  12. SCG – Slice/Cluster-Size Graph Coherent Clusters

  13. Results Coherent Clusters

  14. Function Mapping To Cluster • Tool Maps: source lines and files to clusters. • functions represent particular computation • mapping functions to clusters. • Initial Data: • Multiple functions form a cluster. • Multiple clusters within the same function.

  15. Approximation Constraints • Calculate percentage of pair of nodes whose slices are the same; to that of where the nodes in the pair are in each others slice. •  x and  y, where nodesxand yare nodes of a pdg • | {x,y: S(x) = S(y) }| • | { x,y : x S(y)  y  S(x) }| S() – Backward Slice • The algorithm runs in T(n) = O(n3) hence 30 days • BC – The results is 60%.

  16. Coherent Cluster Dependence Graph 1 2 5 3 4 17 What does this mean?

  17. Coherent Cluster Dependence Graph .. 1 2 5 3 4 Does this mean we are looking at a dependence cluster (or .. MDS)?

  18. 70.43%

  19. Dagstuhl Seminar Beyond Program Slicing • Hypothesis 1: short program slices have fewer faults • Hypothesis 2: code common to many program slices has fewer faults • Hypothesis 3: cliff faces in dependence clusters indicate faults

  20. Ongoing Work • Complete implementation of the tool • Mapping Source Code Constructs to Clusters • Combining Cluster – Larger Clusters • Longitudinalstudy • Faults • Metrics

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