Mining change history for test-plan generation (doctoral symposium)

Regression testing is an essential step in safeguarding the evolution of a system, yet there is often not enough time to exercise all available tests. Identifying the subset of tests that can reveal potential issues introduced by a change is a challenge. It requires identifying the tests that test the chan- ged part of the software. Furthermore, and more challeng- ing, it requires identifying the parts of the system that are potentially affected by that change, a task typically done by means of static program analysis. In this doctoral research, we investigate an alternative approach, using software repos- itory mining. We propose a method that mines the change history of a system to uncover dependencies, and uses these for test-selection and test-prioritization. By reducing the amount of test to exercise, and limiting time spend on test- plan creation (i.e., selecting and prioritizing tests), the aim of our approach is to increase cost-effectiveness of software regression testing.

[1]  Andreas Zeller,et al.  Mining Version Histories to Guide Software Changes , 2004 .

[2]  LinWeiyang,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2002 .

[3]  Vijay V. Raghavan,et al.  TRARM-RelSup: Targeted Rare Association Rule Mining Using Itemset Trees and the Relative Support Measure , 2012, ISMIS.

[4]  Philippe Fournier-Viger,et al.  MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining , 2013, ADMA.

[5]  Vijay V. Raghavan,et al.  Itemset Trees for Targeted Association Querying , 2003, IEEE Trans. Knowl. Data Eng..

[6]  Mohammed J. Zaki.  Scalable Algorithms for Association Mining , 2000, IEEE Trans. Knowl. Data Eng..

[7]  Sergio A. Alvarez,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2004, Data Mining and Knowledge Discovery.

[8]  Vijay V. Raghavan,et al.  The Item-Set Tree: A Data Structure for Data Mining , 1999, DaWaK.

[9]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[10]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[11]  Chengqi Zhang,et al.  Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support , 2009, Expert Syst. Appl..

[12]  Laurie A. Williams,et al.  Prioritization of Regression Tests using Singular Value Decomposition with Empirical Change Records , 2007, The 18th IEEE International Symposium on Software Reliability (ISSRE '07).

[13]  Frank C. Lin,et al.  Integrated Software Vulnerability and Security Functionality Assessment , 2007, The 18th IEEE International Symposium on Software Reliability (ISSRE '07).

[14]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[15]  Jiawei Han,et al.  Mining top-k frequent closed patterns without minimum support , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[16]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[17]  Ada Wai-Chee Fu,et al.  Mining frequent itemsets without support threshold: with and without item constraints , 2004, IEEE Transactions on Knowledge and Data Engineering.

[18]  Gail C. Murphy,et al.  Predicting source code changes by mining change history , 2004, IEEE Transactions on Software Engineering.

[19]  Leon Moonen,et al.  Crossing the boundaries while analyzing heterogeneous component-based software systems , 2011, 2011 27th IEEE International Conference on Software Maintenance (ICSM).

[20]  Mark Harman,et al.  Regression testing minimization, selection and prioritization: a survey , 2012, Softw. Test. Verification Reliab..