Logo PTI Logo FedCSIS

Communication Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 37

An Empirical Framework for Software Aging-Related Bug Prediction using Weighted Extreme Learning Machine

, , , ,

DOI: http://dx.doi.org/10.15439/2023F9248

Citation: Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 37, pages 187–194 ()

Full text

Abstract. Software ageing (SA) related bugs highlight the issue of software failure within continuously running systems, resulting in a decline in quality, system crashes, resource misuse, and more. To mitigate these bugs, software companies employ various techniques, including code reviews, bug-tracking systems deployment, and thorough testing. Nevertheless, the identification of aging-related bugs remains challenging through these conventional approaches. To address this predicament, early prediction of the affected software regions due to runtime failures can be immensely valuable for software quality assurance teams. By accurately identifying the vulnerable areas, these teams can strategically allocate their limited resources during the testing and maintenance processes. This proactive approach ensures a more efficient and effective bug detection and resolution, enhancing overall software reliability and performance. This study aims to develop aging-related bug prediction models using source code metrics as input. In particular, our objective is to investigate metrics selections, data balancing, and weighted ELM to detect software runtime failure. Experimental results show that ELM with data imbalance SMOTE technique performs the best compared to weighted ELM for addressing the class imbalance problem. The weighted ELM and ELM + SMOTE can predict SA bugs, and these models can be applied to the future releases of software projects for online failure prediction well in advance. The experimental finding shows that the models trained using normal ELM with SMOTE data sampling techniques have significant performance improvement.

References

  1. S. R. Chidamber and C. F. Kemerer, “A metrics suite for object oriented design,” IEEE Transactions on software engineering, vol. 20, no. 6, pp. 476–493, 1994.
  2. M. K. Thota, F. H. Shajin, P. Rajesh et al., “Survey on software defect prediction techniques,” International Journal of Applied Science and Engineering, vol. 17, no. 4, pp. 331–344, 2020.
  3. R. Pietrantuono and S. Russo, “A survey on software aging and rejuvenation in the cloud,” Software Quality Journal, vol. 28, no. 1, pp. 7–38, 2020.
  4. R. Matias, B. E. Costa, and A. Macedo, “Monitoring memory-related software aging: An exploratory study,” in 2012 IEEE 23rd International Symposium on Software Reliability Engineering Workshops. IEEE, 2012, pp. 247–252.
  5. S. S. Chouhan, S. S. Rathore, and R. Choudhary, “A study of agingrelated bugs prediction in software system,” in Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. Springer, 2021, pp. 49–61.
  6. J. Dai, J. Chen, Y. Liu, and H. Hu, “Novel multi-label feature selection via label symmetric uncertainty correlation learning and feature redundancy evaluation,” Knowledge-Based Systems, vol. 207, p. 106342, 2020.
  7. A. G. Karegowda, A. Manjunath, and M. Jayaram, “Comparative study of attribute selection using gain ratio and correlation based feature selection,” International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 271–277, 2010.
  8. W. Zong, G.-B. Huang, and Y. Chen, “Weighted extreme learning machine for imbalance learning,” Neurocomputing, vol. 101, pp. 229–242, 2013.
  9. K. Li, X. Kong, Z. Lu, L. Wenyin, and J. Yin, “Boosting weighted elm for imbalanced learning,” Neurocomputing, vol. 128, pp. 15–21, 2014.
  10. Y. Huang, C. Kintala, N. Kolettis, and N. D. Fulton, “Software rejuvenation: Analysis, module and applications,” in Twenty-fifth international symposium on fault-tolerant computing. Digest of papers. IEEE, 1995, pp. 381–390.
  11. J. Alonso, R. Matias, E. Vicente, A. Maria, and K. S. Trivedi, “A comparative experimental study of software rejuvenation overhead,” Performance Evaluation, vol. 70, no. 3, pp. 231–250, 2013.
  12. R. Matias and J. Paulo Filho, “An experimental study on software aging and rejuvenation in web servers,” in 30th Annual International Computer Software and Applications Conference (COMPSAC’06), vol. 1. IEEE, 2006, pp. 189–196.
  13. J. Araujo, R. Matos, P. Maciel, R. Matias, and I. Beicker, “Experimental evaluation of software aging effects on the eucalyptus cloud computing infrastructure,” in Proceedings of the middleware 2011 industry track workshop, 2011, pp. 1–7.
  14. J. Zheng, H. Okamura, L. Li, and T. Dohi, “A comprehensive evaluation of software rejuvenation policies for transaction systems with markovian arrivals,” IEEE Transactions on Reliability, vol. 66, no. 4, pp. 1157–1177, 2017.
  15. N. Padhy, R. Singh, and S. C. Satapathy, “Enhanced evolutionary computing based artificial intelligence model for web-solutions software reusability estimation,” Cluster Computing, vol. 22, no. 4, pp. 9787–9804, 2019.
  16. S. Sharma and S. Kumar, “Analysis of ensemble models for aging related bug prediction in software systems.” in ICSOFT, 2018, pp. 290–297.
  17. M. Khanna, M. Aggarwal, and N. Singhal, “Empirical analysis of artificial immune system algorithms for aging related bug prediction,” in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1. IEEE, 2021, pp. 692–697.
  18. F. Qin, X. Wan, and B. Yin, “An empirical study of factors affecting cross-project aging-related bug prediction with tlap,” Software Quality Journal, vol. 28, no. 1, pp. 107–134, 2020.
  19. A. G. Koru and H. Liu, “An investigation of the effect of module size on defect prediction using static measures,” in Proceedings of the 2005 workshop on Predictor models in software engineering, 2005, pp. 1–5.
  20. T. Mende, “Replication of defect prediction studies: problems, pitfalls and recommendations,” in Proceedings of the 6th International Conference on Predictive Models in Software Engineering, 2010, pp. 1–10.
  21. T. Mende and R. Koschke, “Revisiting the evaluation of defect prediction models,” in Proceedings of the 5th International Conference on Predictor Models in Software Engineering, 2009, pp. 1–10.
  22. A. Bovenzi, D. Cotroneo, R. Pietrantuono, and S. Russo, “Workload characterization for software aging analysis,” in 2011 IEEE 22nd International Symposium on Software Reliability Engineering. IEEE, 2011, pp. 240–249.