TOWARD A NON INVASIVE CONTROL OF APPLICATIONS - A Biomedical Approach to Failure Prediction

Ilenia Fronza, Alberto Sillitti, Giancarlo Succi, Jelena Vlasenko

2011

Abstract

Developing software without failures is indeed important. Still, it is also important to detect as soon as possible when a running application is likely to fail, so that corrective actions can be taken. Following the guidelines of Agile Methods, the goal of our research is to develop a statistical prediction model for failures that does not require any additional effort on the side of the developers of an application; the key concept is that the developers concentrate on the code and we use the information that is naturally generated by the running application to assess whether an application is likely to fail. So the developers concentrate only on providing direct value to the customer and then the model takes care of informing the environment of the possible crash. The proposed model uses as input data that is commonly produced by developers: the log files. The statistical prediction model employed comes from biomedical studies about cancer survival prediction based on gene expression profiles where gene expression measurements and survival times of previous patients are used to predict future patients' survival. One of the most prominent models is the Cox Proportional Hazards (PH) model. In this work, we draw a parallel between our context and the biomedical one; we consider types of operations as genes, and operations and their multiplicity in the sequence as expression profiles. Then, we identify signature operations applying the above mentioned Cox PH model. We perform a prototypical analysis using real-world data to assess the suitability of our approach. We estimate the confidence interval of our results using Bootstrap.

References

  1. Agerbo, E. 2007. High Income, Employment, Postgraduate Education, and Marriage: a Suicidal Cocktail among Psychiatric Patients. Archives of General Psychiatry, 64, 12, 1377-1384.
  2. Agerbo, E. 2007. High Income, Employment, Postgraduate Education, and Marriage: a Suicidal Cocktail among Psychiatric Patients. Archives of General Psychiatry, 64, 12, 1377-1384.
  3. Barros, C. P. and Machado, L. P. 2010. The Length of Stay in Tourism. Annals of Tourism Research, 37, 3, 692-706.
  4. Barros, C. P. and Machado, L. P. 2010. The Length of Stay in Tourism. Annals of Tourism Research, 37, 3, 692-706.
  5. Benda, B. 2005. Gender Differences in Life-Course Theory of Recidivism: A Survival Analysis. International Journal of Offender Therapy and Comparative Criminology, 49, 3, 325-342.
  6. Benda, B. 2005. Gender Differences in Life-Course Theory of Recidivism: A Survival Analysis. International Journal of Offender Therapy and Comparative Criminology, 49, 3, 325-342.
  7. Bøvelstad, H. M., Nygård, S., Størvold, H. L., Aldrin, M., Borgan, Ø., Frigessi, A., and Lingjaerde, O. C. 2007. Predicting Survival from Microarray Data: a Comparative Study. Bioinformatics, 23, 16, 2080- 2087.
  8. Bøvelstad, H. M., Nygård, S., Størvold, H. L., Aldrin, M., Borgan, Ø., Frigessi, A., and Lingjaerde, O. C. 2007. Predicting Survival from Microarray Data: a Comparative Study. Bioinformatics, 23, 16, 2080- 2087.
  9. Bøvelstad, H. M. 2010. Survival Prediction from HighDimensional Genomic Data. Doctoral Thesis. University of Oslo.
  10. Bøvelstad, H. M. 2010. Survival Prediction from HighDimensional Genomic Data. Doctoral Thesis. University of Oslo.
  11. Chen,Y., Zhang, H., and Zhu, P. 2009. Study of Customer Lifetime Value Model Based on Survival-Analysis Methods. In World Congress on Computer Science and Information Engineering (Los Angeles, USA, March 31 - April 02, 2009), 266-270.
  12. Chen,Y., Zhang, H., and Zhu, P. 2009. Study of Customer Lifetime Value Model Based on Survival-Analysis Methods. In World Congress on Computer Science and Information Engineering (Los Angeles, USA, March 31 - April 02, 2009), 266-270.
  13. Coman, I. D., Sillitti, A., and Succi, G. 2009. A case-study on using an Automated In-process Software Engineering Measurement and Analysis system in an industrial environment. In ICSE'09: International Conference on Software Engineering (Vancouver, Canada, May 16-24 May, 2009), pp. 89-99.
  14. Coman, I. D., Sillitti, A., and Succi, G. 2009. A case-study on using an Automated In-process Software Engineering Measurement and Analysis system in an industrial environment. In ICSE'09: International Conference on Software Engineering (Vancouver, Canada, May 16-24 May, 2009), pp. 89-99.
  15. Cox, D.R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society Series B, 34, 187-220.
  16. Cox, D.R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society Series B, 34, 187-220.
  17. Collett, D. 1994. Model ling Survival Data in Medical Research. Chapman&Hall, London.
  18. Collett, D. 1994. Model ling Survival Data in Medical Research. Chapman&Hall, London.
  19. Dobson, S., Denazis, S., Fernández, A., Gaiti, D., Gelenbe, E., Massacci, F., Nixon, P., Saffre, F., Schmidt, N., and Zambonelli, F. 2006. A Survey of Autonomic Communications. ACM Transactions on Autonomous and Adaptive Systems, 1, 2, 223-259.
  20. Dobson, S., Denazis, S., Fernández, A., Gaiti, D., Gelenbe, E., Massacci, F., Nixon, P., Saffre, F., Schmidt, N., and Zambonelli, F. 2006. A Survey of Autonomic Communications. ACM Transactions on Autonomous and Adaptive Systems, 1, 2, 223-259.
  21. Efron, B. 1987. Better Bootstrap Confidence Interval. Journal of the American Statistical Association, 82, 171-200.
  22. Efron, B. 1987. Better Bootstrap Confidence Interval. Journal of the American Statistical Association, 82, 171-200.
  23. Efron, B. and Tibshirani, R.: An Introduction to the Bootstrap. Chapman & Hall (1993).
  24. Efron, B. and Tibshirani, R.: An Introduction to the Bootstrap. Chapman & Hall (1993).
  25. Eliassen, A. H., Hankinson, S. E, Rosner, B., Holmes, M. D., and Willett, W. C. 2010. Physical Activity and Risk of Breast Cancer Among Postmenopausal Women. Archives of Internal Medicine, 170, 19, 1758- 1764.
  26. Eliassen, A. H., Hankinson, S. E, Rosner, B., Holmes, M. D., and Willett, W. C. 2010. Physical Activity and Risk of Breast Cancer Among Postmenopausal Women. Archives of Internal Medicine, 170, 19, 1758- 1764.
  27. Fisher, N. I. and Hall, P. 1991. Bootstrap algorithms for small samples. Journal of Statistical Planning and Inference, 27, 157-169.
  28. Fisher, N. I. and Hall, P. 1991. Bootstrap algorithms for small samples. Journal of Statistical Planning and Inference, 27, 157-169.
  29. Hao, K., Luk, J. M., Lee, N. P. Y., Mao, M., Zhang, C., Ferguson, M. D., Lamb, J., Dai, H., Ng, I. O., Sham, P.C., and Poon, R.T.P. 2009. Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters. BMC Cancer, 9, 398-400.
  30. Hao, K., Luk, J. M., Lee, N. P. Y., Mao, M., Zhang, C., Ferguson, M. D., Lamb, J., Dai, H., Ng, I. O., Sham, P.C., and Poon, R.T.P. 2009. Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters. BMC Cancer, 9, 398-400.
  31. Hosmer, D. W., Lemeshow, S., and May, S. 2008. Applied survival analysis: Regression modeling of time to event data. Wiley.
  32. Hosmer, D. W., Lemeshow, S., and May, S. 2008. Applied survival analysis: Regression modeling of time to event data. Wiley.
  33. Kalbfleisch, J. D. and Prentice, R. L. 2002. The statistical analysis of failure time data. Wiley.
  34. Kalbfleisch, J. D. and Prentice, R. L. 2002. The statistical analysis of failure time data. Wiley.
  35. Kleinbaum, D. G. and Klein, M. 2005. Survival analysis: a self-learning test (Statistics for Biology and Health). Springer.
  36. Kleinbaum, D. G. and Klein, M. 2005. Survival analysis: a self-learning test (Statistics for Biology and Health). Springer.
  37. Lee E. T. and Wang, J. W. 2003. Statistical methods for survival data analysis. Wiley.
  38. Lee E. T. and Wang, J. W. 2003. Statistical methods for survival data analysis. Wiley.
  39. Li, Z., Zhou, S., Choubey, S., and Sievenpiper, C. 2007. Failure event prediction using the Cox proportional hazard model driven by frequent failure sequences. IEE Transactions, 39, 3, 303-315.
  40. Li, Z., Zhou, S., Choubey, S., and Sievenpiper, C. 2007. Failure event prediction using the Cox proportional hazard model driven by frequent failure sequences. IEE Transactions, 39, 3, 303-315.
  41. Liker, J. 2003. The Toyota Way. 14 Management Principles from the World's Greatest Manufacturer. McGraw Hill.
  42. Liker, J. 2003. The Toyota Way. 14 Management Principles from the World's Greatest Manufacturer. McGraw Hill.
  43. Mannila, H., Toinoven, H., and Verkamo, A. I. 1997. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1, 259-289.
  44. Mannila, H., Toinoven, H., and Verkamo, A. I. 1997. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1, 259-289.
  45. Müller, H. A., Kienle, H. M., and Stege, U. 2009. Autonomic Computing: Now You See It, Now You Don't-Design and Evolution of Autonomic Software Systems. In: De Lucia, A., Ferrucci, F. (eds.): Software Engineering International Summer School Lectures: University of Salerno 2009. LNCS, vol. 5413, pp. 32-54. Springer-Verlag.
  46. Müller, H. A., Kienle, H. M., and Stege, U. 2009. Autonomic Computing: Now You See It, Now You Don't-Design and Evolution of Autonomic Software Systems. In: De Lucia, A., Ferrucci, F. (eds.): Software Engineering International Summer School Lectures: University of Salerno 2009. LNCS, vol. 5413, pp. 32-54. Springer-Verlag.
  47. Pandalai, D. N. and Holloway, L. E. 2000. Template languages for fault monitoring of timed discrete event processes. IEEE Transactions on Automatic Control, 45, 5, 868-882.
  48. Pandalai, D. N. and Holloway, L. E. 2000. Template languages for fault monitoring of timed discrete event processes. IEEE Transactions on Automatic Control, 45, 5, 868-882.
  49. Persson, I. 2002. Essays on the Assumptions of Proportional Hazards in Cox Regression. Doctoral Thesis. Uppsala University.
  50. Persson, I. 2002. Essays on the Assumptions of Proportional Hazards in Cox Regression. Doctoral Thesis. Uppsala University.
  51. Pope, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., and Thurston, G. D. 2002. Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. Journal of the American Medical Association, 27, 9, 1132- 1141.
  52. Pope, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., and Thurston, G. D. 2002. Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. Journal of the American Medical Association, 27, 9, 1132- 1141.
  53. Poppendieck, M. and Poppendieck, T. 2003. Lean software development: an Agile toolkit. Addison Wesley.
  54. Poppendieck, M. and Poppendieck, T. 2003. Lean software development: an Agile toolkit. Addison Wesley.
  55. Sampath, M., Sengupta, R., and Lafortune, S. 1994. Diagnosability of discrete event systems. In: International Conference on Analysis and Optimization of Systems Discrete Event Systems (Sophia, Antipolis, June 15 - 17, 1994), pp. 73-79.
  56. Sampath, M., Sengupta, R., and Lafortune, S. 1994. Diagnosability of discrete event systems. In: International Conference on Analysis and Optimization of Systems Discrete Event Systems (Sophia, Antipolis, June 15 - 17, 1994), pp. 73-79.
  57. Schmidt, P. and Witte, A. D. 1989. Predicting Criminal Recidivism Using “Split Population” Survival Time Models. Journal of Econometrics, 40, 1, 141-159.
  58. Schmidt, P. and Witte, A. D. 1989. Predicting Criminal Recidivism Using “Split Population” Survival Time Models. Journal of Econometrics, 40, 1, 141-159.
  59. Sherkat, D. E. and Ellison, C. G.: Structuring the ReligionEnvironment Connection: Religious Influences on Environmental Concern and Activism. Journal for the Scientific Study of Religion, 46, 71-85 (2007).
  60. Sherkat, D. E. and Ellison, C. G.: Structuring the ReligionEnvironment Connection: Religious Influences on Environmental Concern and Activism. Journal for the Scientific Study of Religion, 46, 71-85 (2007).
  61. Srinivasan, V. S. and Jafari, M. A. 1993. Fault detection/monitoring using time petri nets. IEEE Transactions on System, Man and Cybernetics, 23, 4, 1155-1162.
  62. Srinivasan, V. S. and Jafari, M. A. 1993. Fault detection/monitoring using time petri nets. IEEE Transactions on System, Man and Cybernetics, 23, 4, 1155-1162.
  63. Wedel, M., Jensen, U., and Göhner, P. 2008. Mining software code repositories and bug databases using survival analysis models. In: 2nd ACM-IEEE international symposium on Empirical software engineering and measurement (Kaiserslautern, Germany, October 09 - 10, 2008), pp. 282-284.
  64. Wedel, M., Jensen, U., and Göhner, P. 2008. Mining software code repositories and bug databases using survival analysis models. In: 2nd ACM-IEEE international symposium on Empirical software engineering and measurement (Kaiserslautern, Germany, October 09 - 10, 2008), pp. 282-284.
  65. Yanaihara, N. et al. 2006. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell, 9, 3, 189-198.
  66. Yanaihara, N. et al. 2006. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell, 9, 3, 189-198.
  67. Yu, S. L. et al. 2008. MicroRNA Signature Predicts Survival and Relapse in Lung Cancer. Cancer Cell, 13, 1, 48-57.
  68. Yu, S. L. et al. 2008. MicroRNA Signature Predicts Survival and Relapse in Lung Cancer. Cancer Cell, 13, 1, 48-57.
  69. Zheng, Z., Lan, Z., Park, B. H., and Geist, A. 2009. System log pre-processing to improve failure prediction. In: 39th Annual IEEE/IFIP International Conference on Dependable Systems & Networks (Lisbon, Portugal, June 29 - July 2), pp. 572-577.
  70. Zheng, Z., Lan, Z., Park, B. H., and Geist, A. 2009. System log pre-processing to improve failure prediction. In: 39th Annual IEEE/IFIP International Conference on Dependable Systems & Networks (Lisbon, Portugal, June 29 - July 2), pp. 572-577.
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Paper Citation


in Harvard Style

Fronza I., Sillitti A., Succi G. and Vlasenko J. (2011). TOWARD A NON INVASIVE CONTROL OF APPLICATIONS - A Biomedical Approach to Failure Prediction . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-54-6, pages 83-91. DOI: 10.5220/0003493000830091


in Harvard Style

Fronza I., Sillitti A., Succi G. and Vlasenko J. (2011). TOWARD A NON INVASIVE CONTROL OF APPLICATIONS - A Biomedical Approach to Failure Prediction . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-54-6, pages 83-91. DOI: 10.5220/0003493000830091


in Bibtex Style

@conference{iceis11,
author={Ilenia Fronza and Alberto Sillitti and Giancarlo Succi and Jelena Vlasenko},
title={TOWARD A NON INVASIVE CONTROL OF APPLICATIONS - A Biomedical Approach to Failure Prediction},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2011},
pages={83-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003493000830091},
isbn={978-989-8425-54-6},
}


in Bibtex Style

@conference{iceis11,
author={Ilenia Fronza and Alberto Sillitti and Giancarlo Succi and Jelena Vlasenko},
title={TOWARD A NON INVASIVE CONTROL OF APPLICATIONS - A Biomedical Approach to Failure Prediction},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2011},
pages={83-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003493000830091},
isbn={978-989-8425-54-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - TOWARD A NON INVASIVE CONTROL OF APPLICATIONS - A Biomedical Approach to Failure Prediction
SN - 978-989-8425-54-6
AU - Fronza I.
AU - Sillitti A.
AU - Succi G.
AU - Vlasenko J.
PY - 2011
SP - 83
EP - 91
DO - 10.5220/0003493000830091


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - TOWARD A NON INVASIVE CONTROL OF APPLICATIONS - A Biomedical Approach to Failure Prediction
SN - 978-989-8425-54-6
AU - Fronza I.
AU - Sillitti A.
AU - Succi G.
AU - Vlasenko J.
PY - 2011
SP - 83
EP - 91
DO - 10.5220/0003493000830091