Data mining and cross-checking of execution traces: a re-interpretation of Jones, Harrold and Stasko test information

ABSTRACT

The current trend in debugging and testing is to cross-check information collected during several executions. Jones et al., for example, propose to use the instruction coverage of passing and failing runs in order to visualize suspicious statements. This seems promising but lacks a formal justification. In this paper, we show that the method of Jones et al. can be re-interpreted as a data mining procedure. More particularly, they define an indicator which characterizes association rules between data. With this formal framework we are able to explain intrinsic limitations of the above indicator.

REFERENCES

• 1: Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
• 2: 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
• 3: Sergey Brin , Rajeev Motwani , Jeffrey D. Ullman , Shalom Tsur, Dynamic itemset counting and implication rules for market basket data, Proceedings of the 1997 ACM SIGMOD international conference on Management of data, p.255-264, May 11-15, 1997, Tucson, Arizona, United States
• 4: T. Denmat, M. Ducassé, and O. Ridoux. Data mining and cross-checking of execution traces. Are-interpretation of Jones, Harrold and Stasko test information visualization (Long version). Research Report RR-5661, INRIA, August 2005. http://www.inria.fr/rrrt/rr-5661.html.
• 5: James A. Jones , Mary Jean Harrold , John Stasko, Visualization of test information to assist fault localization, Proceedings of the 24th International Conference on Software Engineering, May 19-25, 2002, Orlando, Florida [doi>10.1145/581339.581397]

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.5 Testing and Debugging

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

General Terms:
Algorithms, Theory