Abstract
This paper deals with an approach based on the similarity of mutants. This similarity is used to reduce the number of mutants to be executed. In order to calculate such a similarity among mutants their structure is used. Each mutant is converted into a hierarchical graph, which represents the program’s flow, variables and conditions. On the basis of this graph form a special graph kernel is defined to calculate similarity among programs. It is then used to predict whether a given test would detect a mutant or not. The prediction is carried out with the help of a classification algorithm. This approach should help to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented in this paper. An example of a program used in experiments is described and the results obtained, especially classification errors, are presented.
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Acree, A.T.: On Mutation, PhD Thesis, Georgia Institute of Technology, Atlanta, Georgia (1980)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD 1993), Washington, DC, pp. 207–216 (1993)
Borgwardt, K.M., Kriegel, H.P.: Shortest-path kernels on graphs. In: ICDM 2005, pp. 74–81 (2005)
Bunke, H., Riesen, K.: Improving vector space embedding of graphs through feature selection algorithms. Pattern Recognition 44(9), 1928–1940 (2011)
Bunke, H., Riesen, K.: Recent advances in graph-based pattern recognition with applications in document analysis. Pattern Recognition 44(5), 1057–1067 (2011)
Budd, T.A.: Mutation Analysis of Program Test Data. PhD Thesis. Yale University, New Haven, Connecticut (1980)
Chein, M., Mugnier, M.L., Simonet, G.: Nested Graphs: A Graph-based Knowledge Representation Model with FOL Semantics. In: Proceedings of the 6th International Conference ”Principles of Knowledge Representation and Reasoning” (KR 1998), Trento, Italy, pp. 524–534. Morgan Kaufmann Publishers (June 1998)
Ji, C., Chen, Z., Xu, B., Zhao, Z.: A Novel Method of Mutation Clustering Based on Domain Analysis. In: Proceedings of the 21st International Conference on Software Engineering and Knowledge Engineering (SEKE 2009), July 1-3. Knowledge Systems Institute Graduate School, Boston (2009)
Chevalley, P.: Applying Mutation Analysis for Object-oriented Programs Using a Reflective Approach. In: Proceedings of the 8th Asia- Pacific Software Engineering Conference (APSEC 2001), Macau, China, December 4-7, p. 267 (2001)
Chevalley, P., Th’evenod-Fosse, P.: A Mutation Analysis Tool for Java Programs. International Journal on Software Tools for Technology Transfer 5(1), 90–103 (2002)
Collins, M., Duffy, N.: New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron. In: Proceedings of ACL 2002 (2002)
DeMillo, R.A., Lipton, R.J., Sayward, F.G.: Hints on Test Data Selection: Help for the Practicing Programmer. Computer 11(4), 34–41 (1978)
Gartner, T.: A survey of kernels for structured data. SIGKDD Explorations 5(1), 49–58 (2003)
Gartner, T.: Kernels for structured data. Series in Machine Perception and Artificial Intelligence. World Scientific (2009)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent-pattern Tree Approach. Data Mining and Knowledge Discovery: An International Journal 8(1), 53–87 (2004)
Haussler, D.: Convolutional kernels on discrete structures. Technical Report UCSC-CRL-99-10, Computer Science Department, UC Santa Cruz (1999)
Hussain, S.: Mutation Clustering, Masters Thesis. King’s College London, Strand, London (2008)
Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)
Jia, Y., Harman, M.: An Analysis and Survey of the Development of Mutation Testing. IEEE Trans. Software Eng., 649–678 (2011)
Kashima, H., Tsuda, K., Inokuchi, A.: Marginalized Kernels Between Labeled Graphs. In: ICML 2003, pp. 321–328 (2003)
Kim, S., Clark, J.A., McDermid, J.A.: Assessing Test Set Adequacy for Object Oriented Programs Using Class Mutation. In: Proceedings of the 3rd Symposium on Software Technology (SoST 1999), Buenos Aires, Argentina, September 8-9 (1999)
Kim, S., Clark, J.A., McDermid, J.A.: The Rigorous Generation of Java Mutation Operators Using HAZOP. In: Proceedings of the 12th International Cofference Software and Systems Engineering and their Applications (ICSSEA 1999), Paris, France, November 29-December 1 (1999)
Kim, S., Clark, J.A., McDermid, J.A.: Class Mutation: Mutation Testing for Object-oriented Programs. In: Proceedings of the Net. Object Days Conference on Object-Oriented Software Systems (2000)
Kim, S., Clark, J.A., McDermid, J.A.: Investigating the effectiveness of object-oriented testing strategies using the mutation method. In: Proceedings of the 1st Workshop on Mutation Analysis (MUTATION 2000), Published in Book Form, as Mutation Testing for the New Century, San Jose, California, October 6-7, pp. 207–225 (2001)
King, K.N., Offutt, A.J.: A Fortran Language System for Mutation- Based Software Testing. Software: Practice and Experience 21(7), 685–718 (1991)
Liwicki, M., Bunke, H., Pittman, J.A., Knerr, S.: Combining diverse systems for handwritten text line recognition. Mach. Vis. Appl. 22(1), 39–51 (2011)
Liwicki, M., Schlapbach, A., Bunke, H.: Automatic gender detection using on-line and off-line information. Pattern Anal. Appl. 14(1), 87–92 (2011)
Preller, A., Mugnier, M.-L., Chein, M.: Logic for Nested Graphs. Computational Intelligence 14(3), 335–357 (1998)
Ma, Y., Offutt, J., Kwon, Y.R.: MuJava: a mutation system for java. In: ICSE, pp. 827–830 (2006)
Mathur, A.P.: Performance, Effectiveness, and Reliability Issues in Software Testing. In: Proceedings of the 5th International Computer Software and Applications Conference (COMPSAC 1979), Tokyo, Japan, September 11-13, pp. 604–605 (1991)
Mathur, A.P., Wong, W.E.: An Empirical Comparison of Mutation and Data Flow Based Test Adequacy Criteria, Purdue University, West Lafayette, Indiana, Technique Report (1993)
Offutt, A.J., Rothermel, G., Zapf, C.: An Experimental Evaluation of Selective Mutation. In: Proceedings of the 15th International Conference on Software Engineering (ICSE 1993), pp. 100–107. IEEE Computer Society Press, Baltimore (1993)
Richiardi, J., Van De Ville, D., Riesen, K., Bunke, H.: Vector Space Embedding of Undirected Graphs with Fixed-cardinality Vertex Sequences for Classification. In: ICPR 2010, pp. 902–905 (2010)
Riesen, K., Bunke, H.: Cluster Ensembles Based on Vector Space Embeddings of Graphs. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 211–221. Springer, Heidelberg (2009)
Riesen, K., Bunke, H.: Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes. In: Perner, P. (ed.) MLDM 2009. LNCS, vol. 5632, pp. 617–631. Springer, Heidelberg (2009)
Riesen, K., Bunke, H.: Reducing the dimensionality of dissimilarity space embedding graph kernels. Eng. Appl. of AI 22(1), 48–56 (2009)
Rozenberg, G.: Handbook of Graph Grammars and Computing by Graph. Transformations. Fundations, vol. 1. World Scientific, London (1997)
Rozenberg, G.: Handbook of Graph Grammars and Computing by Graph. Transformations. Applications, Languages and Tools, vol. 2. World Scientific, London (1999)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. 1-462 (2004)
Schölkopf, B., Smola, A.J.: A Short Introduction to Learning with Kernels. In: Mendelson, S., Smola, A.J. (eds.) Advanced Lectures on Machine Learning. LNCS (LNAI), vol. 2600, pp. 41–64. Springer, Heidelberg (2003)
Schölkopf, B., Smola, A.J.: Learning with kernels. MIT Press, Cambridge (2002)
Strug, B.: Using Kernels on Hierarchical Graphs in Automatic Classification of Designs. In: Jiang, X., Ferrer, M., Torsello, A. (eds.) GbRPR 2011. LNCS, vol. 6658, pp. 335–344. Springer, Heidelberg (2011)
Tsuda, K., Kin, T., Asai, K.: Marginalized kernels for biological sequences. Bioinformatics 18, 268–275
Vishwanathan, S.V.N., Borgwardt, K.M., Schraudolph, N.N.: Fast Computation of Graph Kernels. In: NIPS 2006, pp. 1449–1456 (2006)
Wong, W.E.: On Mutation and Data Flow. PhD Thesis, Purdue University, West Lafayette, Indiana (1993)
Yan, X., Yu, P.S., Han, J.: Substructure Similarity Search in Graph Databases. In: Proc. of 2005 Int. Conf. on Management of Data, SIGMOD 2005 (2005)
Yan, X., Yu, P.S., Han, J.: Graph Indexing: A Frequent Structure-based Approach. In: Proc. of 2004 Int. Conf. on Management of Data, SIGMOD 2004 (2004)
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Strug, J., Strug, B. (2012). Machine Learning Approach in Mutation Testing. In: Nielsen, B., Weise, C. (eds) Testing Software and Systems. ICTSS 2012. Lecture Notes in Computer Science, vol 7641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34691-0_15
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DOI: https://doi.org/10.1007/978-3-642-34691-0_15
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