Data mining library reuse patterns using generalized association rules

ABSTRACT

In this paper, we show how data mining can be used to discover library reuse patterns in existing applications. Specifically, we consider the problem of discovering library classes and member functions that are typically reused in combination by application classes. This paper improves upon our earlier research using “association rules” [8] by taking into account the inheritance hierarchy using “generalized association rules”. This turns out to be a non-trivial but worthwhile endeavor.By browsing generalized association rules, a developer can discover patterns in library usage in a way that takes into account inheritance relationships. For example, such a rule might tell us that application classes that inherit from a particular library class often instantiate another class or one of its descendents. We illustrate the approach using our tool, CodeWeb, by demonstrating characteristic ways in which applications reuse classes in the KDE application framework.

REFERENCES

• 1: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 2: Ming-Syan Chen , Jiawei Han , Philip S. Yu, Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, v.8 n.6, p.866-883, December 1996 [doi>10.1109/69.553155]
• 3: W. W. Cohen. Inductive specification recovery: Understanding software by learning from example behaviors. Automated Software Engineering, 2(2):107-129, 1995.
• 4: Carlos Montes de Oca , Doris L. Carver, Identification of Data Cohesive Subsystems Using Data Mining Techniques, Proceedings of the International Conference on Software Maintenance, p.16, March 16-19, 1998
• 5: Michael D. Ernst , Jake Cockrell , William G. Griswold , David Notkin, Dynamically discovering likely program invariants to support program evolution, Proceedings of the 21st international conference on Software engineering, p.213-224, May 16-22, 1999, Los Angeles, California, United States [doi>10.1145/302405.302467]
• 6: D. Gangopadhyay and S. Mitra. Design by framework completion. Automated Software Engineering, 3:219- 237, 1996.
• 7: N. Megiddo and R. Srikant. Discovering predictive assocation rules. In Proceedings of the 4th International Conference on Knowledge Discovery in Databases and Data Mining, 1998.
• 8: Amir Michail, Data Mining Library Reuse Patterns in User-Selected Applications, Proceedings of the 14th IEEE international conference on Automated software engineering, p.24, October 12-15, 1999
• 9: Gregory Piateski , William Frawley, Knowledge Discovery in Databases, MIT Press, Cambridge, MA, 1991
• 10: Gregor Snelting , Frank Tip, Reengineering class hierarchies using concept analysis, Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering, p.99-110, November 01-05, 1998, Lake Buena Vista, Florida, United States
• 11: Ramakrishnan Srikant , Rakesh Agrawal, Mining Generalized Association Rules, Proceedings of the 21th International Conference on Very Large Data Bases, p.407-419, September 11-15, 1995
• 12: Will Tracz, Confessions of a used program salesman: institutionalizing software reuse, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1995

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques
          Subjects: Software libraries

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
        D.2.11 Software Architectures
            Subjects: Patterns (e.g., client/server, pipeline, blackboard)

  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

General Terms:
Design, Management, Measurement, Performance, Theory

Keywords:
data mining, reuse patterns, software libraries