Intelligent Database Systems Lab N.Y.U.S.T. I. M. 1 Identifying Domain Expertise of Developers from Source Code Presenter : Wu, Jia-Hao Authors : Renuka.

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Presentation transcript:

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 1 Identifying Domain Expertise of Developers from Source Code Presenter : Wu, Jia-Hao Authors : Renuka Sindhgatta KDD (2008)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 22 Outline Motivation Objective Methodology Experiments Conclusion Personal Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation Software engineering and project need the expert to design program.  If we do the method well, we will save much develop time and resource. Identifying domain expertise from source code is useful for us to develop the system.  Because we can know the information from the source code and arrange that task for programmer.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective Identifying developer expertise based on the domain and technical know-how can be a very useful input for allocating projects or tasks. Through the concepts we can define the domain expertise of a developer. The author adrian has primarily worked Message queues, the container and the server implementation.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Methodology SQLQuery.java UpdateRecord.java strSQLJDBCSelectquery… SQLQuery.java 2123… UpdateRecord.java 1142… … …………… Concepts Database persistence and JDBC Resulted in reasonably balanced clusters … Extract concepts clusterpreprocess Extract logs process d1 d2

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Experiments Case Studies  JBoss Application server  Focuses on identifying the key concepts.  Lucene Search Engine Library  The concept dispersion across the developers and time line of expertise gained.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Experiments JBoss Application server  The number of cluster is set to 25 The author scott.stark has primarily worked JDBC, JMS, JMX.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Experiments (Cont.) Lucene Search Engine  The number of clusters is set to 5.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Experiments (Cont.) Evaluation of Extracted Concepts Precision : the degree of accuracy and any errors in the returned concepts. Recall : the degree of completeness and any errors by reason of missing concepts.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Experiments (Cont.) Use of Developer Expertise in Project Task Allocation  A project for allocating tasks using the domain expertise of a developer.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Conclusion Concept dispersion helps in identifying the distribution of knowledge of concepts the developer has gained. Identifying the suitability of a developer for a given development task.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Comments Advantage  The sentence is easy to know the meaning. Drawback  … Application  Knowledge mining with source code.