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Dependency Tracking in software systems

Dependency Tracking in software systems. Presented by: Ashgan Fararooy. Related Papers. Supporting Software Evolution Analysis with Historical Dependencies and Defect Information ( ICSM 2008) A Flexible Framework to Support Collaborative Software Evolution Analysis ( CSMR 2008 )

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Dependency Tracking in software systems

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  1. Dependency Trackingin software systems Presented by: Ashgan Fararooy

  2. Related Papers • Supporting Software Evolution Analysis with Historical Dependencies and Defect Information (ICSM 2008) • A Flexible Framework to Support Collaborative Software Evolution Analysis (CSMR 2008) • Mining Software Repositories for Traceability Links (ICPC 2007) • Tracking Objects to Detect Feature Dependencies (ICPC 2007) • Software Repositories: A Source for Traceability Links (TEFSE-GTC 2007) • Mining Version Archives for Co-changed Lines (ICSE 2006) • Understanding Semantic Impact of Source Code Changes: an Empirical Study

  3. Mining Version Archives for Co-changed Lines Thomas Zimmermann, Sunghun Kim, Andreas Zeller, E. James Whitehead Jr. (ICSE 2006)

  4. Abstract • Files, classes, or methods have frequently been investigated in research on co-change • Present a first study at the level of lines • Annotation Graph which captures how lines evolve over time • More fine-grained software evolution information (based on lines)

  5. Overview • Co-Change: items that are changed together, are related to each other • Any granularity: modules, files, classes, methods • What about more fine-grained items: blocks, lines …

  6. Co-Change in More Fine-Grained Items • Seemed infeasible • Hard to identify across different versions • Line numbers are not suitable identifiers • SCM systems annotation feature is not enough • Line content is not a good identifier either

  7. Annotation Graph Definition: • A multipartite graph where each part corresponds to one version of a file • Within each part/version every line is represented by a single node • Edges between node indicate that a line originates from another: by modification / movement • Node labels (e.g. bold node) indicate a changed line

  8. Annotation Graph

  9. Annotation Graph Construction: • One needs to compare all subsequent revisions of a file • Using the GNU diff tool For computing textual differences • The diff tool returns a list of regions (“hunk”s) that differ in the two files

  10. Annotation Graph Three different kinds of changes: • Modifications • Result in a complete bipartite subgraphs • Additions • Do not result in any edges • Positions of the following lines are updated • Deletions • The same effect as in addition

  11. Annotation Graph Computation: • Creates nodes for each revision and each line • Two approaches • 1- Forward-Directed • 2- Backward-Directed

  12. Annotation Graph Computation (Forward-Directed Algorithm): • Iterate over all pairs of subsequent revisions • For each pair compute the differences (hunks) • Process the hunks to create edges • Exactly one edge between unchanged lines (nodes) • For modified lines all possible edges • For inserted and deleted lines no edges • Label the nodes of the later revision in modifications and additions

  13. Annotation Graph Problem: • Changes that modify large parts of a file • Results in a large number of edges • Not reasonable for evolution analysis

  14. Annotation Graph • Treat large modifications as combined deletions and additions • No creation of edges in the annotation graph

  15. Annotation Graph Recognizing Large Modifications:

  16. Annotating Lines Comparison • Most SCM systems have annotating features for each line providing the latest change information • Annotation graphs can be used to get such information • Furthermore, they provide information on all past changes

  17. Life Cycle of Lines Investigated the life cycle of lines for the Eclipse Project • How frequently are lines changed • Computed for each line the change count • The number of distinct revisions in its annotation • How many developers change a line • What are the most frequently changed lines

  18. Finding Related Lines • Computed related lines using frequent pattern mining • Used transaction ids instead of revision ids • Used Apriori algorithm • Inferred useful association rules

  19. Thank you

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