Separating passing and failing test executions by clustering anomalies
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Baresi, L., & Young, M. (2001). Test oracles. Tech. rep.
Barr, E., Harman, M., McMinn, P., Shahbaz, M., & Yoo, S. (2015). The oracle problem in software testing: A survey. Software Engineering, IEEE Transactions, 41(5), 507–525. doi: 10.1109/TSE.2014.2372785 .
Bowring, J.F., Rehg, J.M., & Harrold, M.J. (2004). Active learning for automatic classification of software behavior. In: Proceedings of the 2004 ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA ’04, pp. 195–205. ACM, New York. doi: 10.1145/1007512.1007539 .
Briand, L. (2008). Novel applications of machine learning in software testing. In: Quality Software, 2008. QSIC ’08. The Eighth International Conference on, pp. 3–10. doi: 10.1109/QSIC.2008.29 .
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Survey, 41(3), 15:1–15:58. doi: 10.1145/1541880.1541882 .
Dickinson, W., Leon, D., & Podgurski, A. (2001). Finding failures by cluster analysis of execution profiles. In: Software Engineering, 2001. ICSE 2001. Proceedings of the 23rd International Conference on, pp. 339–348. doi: 10.1109/ICSE.2001.919107 .
Dickinson, W., Leon, D., & Podgurski, A. (2001). Pursuing failure: The distribution of program failures in a profile space. In: Proceedings of the 8th European Software Engineering Conference Held Jointly with 9th ACM SIGSOFT International Symposium on Foundations of Software Engineering, ESEC/FSE-9, pp. 246–255. ACM, New York. doi: 10.1145/503209.503243 .
Do, H., Elbaum, S., & Rothermel, G. (2005). Supporting controlled experimentation with testing techniques: An infrastructure and its potential impact. Empirical Software Engineering, 10(4), 405–435. doi: 10.1007/s10664-005-3861-2 .
Doong, R. K., & Frankl, P. G. (1994). The astoot approach to testing object-oriented programs. ACM Transactions on Software Engineering and Methodology, 3(2), 101–130. doi: 10.1145/192218.192221 .
Ernst, M. D., Perkins, J. H., Guo, P. J., McCamant, S., Pacheco, C., Tschantz, M. S., et al. (2007). The daikon system for dynamic detection of likely invariants. Science of Computer Programming, 69(1–3), 35–45. doi: 10.1016/j.scico.2007.01.015 .
Han, J., Kamber, M., Pei, J. (2012). 10-cluster analysis: Basic concepts and methods. In: J.H.M. Kamber, J. Pei (Eds.) Data Mining (Third Edition), The Morgan Kaufmann Series in Data Management Systems (third edition ed.), pp. 443–495. Morgan Kaufmann, Boston. doi: 10.1016/B978-0-12-381479-1.00010-1 . http://www.sciencedirect.com/science/article/pii/B9780123814791000101
Hangal, S., & Lam, M.S. (2002). Tracking down software bugs using automatic anomaly detection. In: Proceedings of the 24th International Conference on Software Engineering, ICSE ’02, pp. 291–301. ACM, New York. doi: 10.1145/581339.581377 .
ISTQB. (2016). ISTQB worldwide software testing practices report 2015–2016. Tech. rep. www.istqb.orgv
Jin, W., Orso, A., & Xie, T. (2010). Automated behavioral regression testing. In: 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation, vol. 0, pp. 137–146. doi: 10.1109/ICST.2010.64 .
Miller, B. P., Fredriksen, L., & So, B. (1990). An empirical study of the reliability of unix utilities. Communications of the ACM, 33(12), 32–44. doi: 10.1145/96267.96279 .
Nguyen, C.D., Marchetto, A., & Tonella, P. (2013). Automated oracles: An empirical study on cost and effectiveness. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2013, pp. 136–146. ACM, New York. doi: 10.1145/2491411.2491434 .
Pezzè, M., Zhang, C. (2015). Automated test oracles: A survey. In: Advances in Computers, vol. 95, pp. 1–48. Elsevier, Amsterdam.
Podgurski, A., Leon, D., Francis, P., Masri, W., Minch, M., Sun, J., & Wang, B. (2003). Automated support for classifying software failure reports. In: Proceedings of the 25th International Conference on Software Engineering, ICSE ’03, pp. 465–475. IEEE Computer Society, Washington. http://dl.acm.org/citation.cfm?id=776816.776872
Podgurski, A., Masri, W., McCleese, Y., Wolff, F. G., & Yang, C. (1999). Estimation of software reliability by stratified sampling. ACM Transactions on Software Engineering and Methodology, 8(3), 263–283. doi: 10.1145/310663.310667 .
Rafig, A., & Roper, M. (2015). Building test oracles by clustering failures. In: IEEE/ACM 10th International Workshop on Automation of Software Test (AST 2015), ICSE (Supplemental Proceedings), vol. 2015, pp. 3–7.
Sekar, R., Bendre, M., Dhurjati, D., & Bollineni, P. (2001). A fast automaton-based method for detecting anomalous program behaviors. In: Security and Privacy, 2001. S P 2001. Proceedings. 2001 IEEE Symposium on, pp. 144–155. doi: 10.1109/SECPRI.2001.924295 .
Vanmali, M., Last, M., & Kandel, A. (2002). Using a neural network in the software testing process. International Journal of Intelligent Systems, 17(1), 45–62. doi: 10.1002/int.1002 .
Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Los Altos, CA: Morgan Kaufmann.
Yan, S., Chen, Z., Zhao, Z., Zhang, C., & Zhou, Y. (2010). A dynamic test cluster sampling strategy by leveraging execution spectra information. In: Software Testing, Verification and Validation (ICST), 2010 Third International Conference on, pp. 147–154. doi: 10.1109/ICST.2010.47
Yoo, S., Harman, M., Tonella, P., & Susi, A. (2009). Clustering test cases to achieve effective and scalable prioritisation incorporating expert knowledge. In: Proceedings of International Symposium on Software Testing and Analysis (ISSTA 2009), pp. 201–211. ACM Press, New York.