1. Clustering Software Artifacts Based on Frequent common changes Presented by: Ashgan Fararooy Prepared by: Haroon Malik (Modified)

2. Abstract The clusters of artifacts that are frequently changed changed together are subsystem candidates. Two step method identification of clusters: Extracting Co-Change graph from the version control repository. Computing a layout of the co-changed graph. This reveals the cluster of frequent co-change artifacts.

3. Overview Software artifact : Is an entity that belongs to a software system E.g. A package, a file, a line of code or even a piece of document Version: State of a software artifact at a particular point in time. CVS system stores version of artifacts in a central repository. User of such systems modify local copies of the software artifacts, and check-in their changes to the central repository.

4. Proposed Model Software clustering groups software artifacts into subsystems which are as independent as possible with respected to comprehension, change, reuse etc. Co-change graph model is proposed for clustering software system

5. Proposed Model Co-change Graph: Abstraction of version control repositories. Vertices of this graph are: Software artifacts (Files or Functions) & Change transactions ( Commits in terms of CVS). Edges connect the change transaction with their participating artifacts.

6. Proposed Model Presentation: The result of clustering is not a partition of the graph vertices, but a layout of graph vertices. This layout of the graph refers to position of the graph vertices in two or three dimensional space. Heavily co-changed artifacts closer together. Rarely co-changed artifacts at larger distances. Layout is comprehensive and provides additional information  How clearly Clusters are Separated.  If artifacts are at center of the cluster or rather between two clusters.

7. Proposed Model Contents: Not just arranged in some nice way, but their positions have a well-defined interpretation with respect to their common changes. Two artifacts are placed closer to the degree of that their common change is stronger then random.

8. Co-Change Graph. The graph refers to the common changes of artifacts in version repositories. It can be easily extracted from version repositories. Ensures, that the clustering results have a clear interpretation in terms of repositories. Biases though arbitrary choices (e.g. weight function of values of free parameters) are minimized.

9. Co-Change Graph Change Transaction: It is a coherent sequence of ‘check-in’s of several software artifacts. Software artifacts that participate in the same change transaction are co-changed (commonly changed). The Co-change graph of a give version of repository is an undirected graph (V,E ). The set of vertices V of the co-change graph contains all the software artifacts and all change transaction of the version repository.

10. Co-Change Graph The set of edges E contains the undirected edge {c,a}, if the artifact a was changed by transaction c. Bipartite: It contains no edges that connect two change transaction of two software artifacts.

11. Co-Change Graph For a vertex v of a co-changed graph, the number of its adjacent vertices is called the degree of v and denoted by deg(v) For transaction vertices, the degree gives the number of artifacts that participate in the transaction. For artifacts, the degree gives the number of their changes.

12. Weight Co-change Graph It involves assigning a real number to each edge by weigh function (w) to set of Edges (E) The real number assigned to each edge interprets the importance of the corresponding change. Each edge is give same weight.

13. Condensed Co-Change Graph It is a weighted, undirected graph (V,E,w), for a given repository. Where, the set of vertices V contains all software artifacts in repository. Set of Edges E contains the edge {a,a’}, if the artifact a and a’ were commonly changed by a transaction.

14. Edge-Repulsion Linlog Energy Model This model specify the good graph layout. The basic idea is that in co-change graph edges causes both repulsion and attraction. Every edge will cause same amount of repulsion and attraction. Model helps in creating suitable readable layouts

15. Evaluation The Software system were chosen based on : Size, number of developers, project duration and artifacts in different programming languages Based on familiarity, because the evaluation requires the knowledge of authoritative decompositions

16. Evaluation The co-change graph were extracted on file level A tool cvs2cl2 is used to recover change transaction from CVS repository A calculator for relation generated the co-change graph from transaction ---- CrocoPat Duration, total changes indeed all number were obtained with tool Stat CVS Layout was computed using utomatically usig Edge repulsion linlog energy model

17. Artifacts in the CrocoPat repository

18. Artifacts in the Rabbit repository

19. Artifacts in the Blast repository

20. Conclusions Introduced a new method for clustering software artifacts. Defined the co-change graph as underlying formal model Evaluated our method on three example software systems with different types of documents and source code in several programming languages