Using Evolutionary Process for Cross-Version Software Defect Prediction

doi:10.23940/ijpe.19.09.p22.24842493

Abstract

Abstract: Cross-version defect prediction can effectively reduce the construction cost of a model, which is of great significance for understanding the causes of defects in subsequent software versions and improving the quality of software products. The objective of this work is to validate the feasibility of the cross-version defect predictor using the evolutionary process in different scenarios and to investigate practical guidelines for the choice of evolutionary process and historical data of a given project. Firstly, we verified the effectiveness of the evolutionary process attributes in the cross-version prediction model. Then, we analyzed the cross-version model prediction performance based on multi-version historical data. Finally, multiple benchmark models were selected for experimental comparison to further verify the effectiveness of the proposed method. The experimental conclusions can not only effectively reduce the construction cost of the defect prediction model in the version evolution scenarios, but also help developers understand the defect generation mechanism in the subsequent versions and improve the software development process in a targeted way.

Key words: cross-version defect prediction, evolution process, machine learning, multi-version data driven

Yong Li, Zhandong Liu, and Haijun Zhang. Using Evolutionary Process for Cross-Version Software Defect Prediction [J]. Int J Performability Eng, 2019, 15(9): 2484-2493.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. Q. Song, Y. Guo,M. Shepperd, “A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction,” IEEE Transactions on Software Engineering, p. 1, 2018
2. Y. Li, Z. Huang, Y. Wang,B. Fang, “Evaluating Data Filter on Cross-Project Defect Prediction: Comparison and Improvements,”IEEE Access, Vol. 5, pp. 25646-25656, 2017
3. B. Turhan, A. T. Mısırlı,A. Bener, “Empirical Evaluation of the Effects of Mixed Project Data on Learning Defect Predictors,”Information and Software Technology, Vol. 55, pp. 1101-1118, 2013
4. C. Catal, “A Comparison of Semi-Supervised Classification Approaches for Software Defect Prediction,”Journal of Intelligent Systems, Vol. 23, pp. 75-82, 2014
5. S. Shukla, T. Radhakrishnan, K. Muthukumaran,L. B. M.Neti, “Multi-Objective Cross-Version Defect Prediction,”Soft Computing, Vol. 22, pp. 1959-1980, 2018
6. J. Erickson, K. Lyytinen, K. Siau,K. Siau, “Agile Modeling, Agile Software Development, and Extreme Programming: The State of Research,”Journal of Database Management, Vol. 16, pp. 88-99, 2017
7. H. M. Olague, L. H. Etzkorn, S. Gholston,S. Quattlebaum, “Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed using Highly Iterative or Agile Software Development Processes,”IEEE Transactions on Software Engineering, Vol. 33, pp. 402-419, 2007
8. M. O. Elish, A. H.Al-Yafei, and M. Al-Mulhem, “Empirical Comparison of Three Metrics Suites for Fault Prediction in Packages of Object-Oriented Systems: A Case Study of Eclipse,”Advances in Engineering Software, Vol. 42, pp. 852-859, 2011
9. R. Shatnawi and W. Li, “The Effectiveness of Software Metrics in Identifying Error-Prone Classes in Post-Release Software Evolution Process,”Journal of Systems and Software, Vol. 81, pp. 1868-1882, 2008
10. F. Rahman and P. Devanbu, “How, and Why, Process Metrics are Better,” inProceedings of the 2013 International Conference on Software Engineering, pp. 432-441, San Francisco, CA, USA, 2013
11. K. E. Bennin, K. Toda, Y. Kamei, J. Keung, A. Monden,N. Ubayashi, “Empirical Evaluation of Cross-Release Effort-Aware Defect Prediction Models,” inProceedings of 2016 IEEE International Conference on Software Quality, Reliability and Security (QRS), pp. 214-221, IEEE, 2016
12. S. Amasaki, “On Applicability of Cross-project Defect Prediction Method for Multi-Versions Projects,” inProceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering, pp. 93-96, Toronto, Canada, 2017
13. Y. Kastro and A. B. Bener, “A Defect Prediction Method for Software Versioning,”Software Quality Control, Vol. 16, pp. 543-562, 2008
14. E. Weyuker, T. Ostrand,R. Bell, “Do Too Many Cooks Spoil the Broth? Using the Number of Developers to Enhance Defect Prediction Models,”Empirical Software Engineering, Vol. 13, pp. 539-559, 2008
15. S. Kpodjedo, F. Ricca, P. Galinier, Y. Guéhéneuc,G. Antoniol, “Design Evolution Metrics for Defect Prediction in Object Oriented Systems,”Empirical Software Engineering, Vol. 16, pp. 141-175, 2011
16. D. D.Wang and Q. Wang, “'Improving the Performance of Defect Prediction based on Evolution Data,”Journal of Software, pp. 3014-3029, 2016
17. R. Moser, W. Pedrycz,G. Succi, “A Comparative Analysis of the Efficiency of Change Metrics and Static Code Attributes for Defect Prediction,” inProceedings of the 30th International Conference on Software Engineering, pp. 181-190, Leipzig, Germany, 2008
18. A. Okutan and O. T. Yıldız, “Software Defect Prediction using Bayesian Networks,”Empirical Software Engineering, Vol. 19, pp. 154-181, 2014
19. S. Matsumoto, Y. Kamei, A. Monden, K. Matsumoto,M. Nakamura, “An Analysis of Developer Metrics for Fault Prediction,” inProceedings of the 6th International Conference on Predictive Models in Software Engineering, pp. 1-9, Romania, 2010
20. R. M. Bell, T. J. Ostrand,E. J. Weyuker, “Does Measuring Code Change Improve Fault Prediction?” inProceedings of the 7th International Conference on Predictive Models in Software Engineering, pp. 1-8, Banff, Alberta, Canada, 2011
21. T. M. Khoshgoftaar, E. B. Allen, R. Halstead, G. P. Trio,R. M. Flass, “Using Process History to Predict Software Quality,” Computer, Vol. 31, No. 4, pp. 66-72, 1998
22. T. Menzies, B. Caglayan, E. Kocaguneli, J. Krall, F. Peters,B. Turhan, “The Promise Repository of Empirical Software Engineering Data,” West Virginia University, Department of Computer Science, 2012
23. B. Turhan and A. Bener, “Analysis of Naive Bayes' Assumptions on Software Fault Data: An Empirical Study,”Data and Knowledge Engineering, Vol. 68, pp. 278-290, 2009
24. T. Menzies, J. Greenwald,A. Frank, “Data Mining Static Code Attributes to Learn Defect Predictors,”IEEE Transactions on Software Engineering, Vol. 33, pp. 2-13, 2007

[1]	C. Rohith Bhat and Madhusundar Nelson. Artificial Intelligence Based Credit Card Fraud Detection for Online Transactions Optimized with Sparrow Search Algorithm [J]. Int J Performability Eng, 2023, 19(9): 624-632.
[2]	Savita Khurana, Gaurav Sharma, and Bhawna Sharma. Hybrid Machine Learning Model for Load Prediction in Cloud Environment [J]. Int J Performability Eng, 2023, 19(8): 507-515.
[3]	K. Eswara Rao, Bala Murali Pydi, T. Panduranga Vital, P. Annan Naidu, U. D. Prasann, and T. Ravikumar. An Advanced Machine Learning Approach for Student Placement Prediction and Analysis [J]. Int J Performability Eng, 2023, 19(8): 536-546.
[4]	Babaljeet Kaur and Shalli Rani. Are the Customers Receiving Exact Recommendations from the E-Commerce Companies? Towards the Identification of Gray Sheep Users Using Personality Parameters [J]. Int J Performability Eng, 2023, 19(7): 425-433.
[5]	Kshitij Kumar Sinha, Manoj Mathur, and Arun Sharma. Suitability Index Prediction for Residential Apartments Through Machine Learning [J]. Int J Performability Eng, 2023, 19(7): 434-442.
[6]	Manpreet Kaur and Shalli Rani. Recommender System: Towards Identification of Shilling Attacks in Rating System Using Machine Learning Algorithms [J]. Int J Performability Eng, 2023, 19(7): 443-451.
[7]	Srishti Bhugra and Puneet Goswami. Exploratory Review of Machine Learning-Based Software Component Reusability Prediction [J]. Int J Performability Eng, 2023, 19(7): 452-461.
[8]	Harsha Gaikwad, Sanil Gandhi, Arvind Kiwelekar, and Manjushree Laddha. Analyzing Brain Signals for Predicting Students’ Understanding of Online Learning: A Machine Learning Approach [J]. Int J Performability Eng, 2023, 19(7): 462-470.
[9]	Rakesh Kumar, Sunny Arora, Ashima Arya, Neha Kohli, Vaishali Arya, and Ekta Singh. Ensemble Learning for Appraising English Text Readability using Gompertz Function [J]. Int J Performability Eng, 2023, 19(6): 388-396.
[10]	Pranshu Kumar Soni and Leema Nelson. PCP: Profit-Driven Churn Prediction using Machine Learning Techniques in Banking Sector [J]. Int J Performability Eng, 2023, 19(5): 303-311.
[11]	Ramneet Kaur, Deepali Gupta, and Mani Madhukar. Learner-Centric Hybrid Filtering-Based Recommender System for Massive Open Online Courses [J]. Int J Performability Eng, 2023, 19(5): 324-333.
[12]	Mahima Yadav and Ishan Kumar. Image Processing-Based Transliteration from Hindi to English [J]. Int J Performability Eng, 2023, 19(5): 334-341.
[13]	Harshita Batra and Leema Nelson. DCADS: Data-Driven Computer Aided Diagnostic System using Machine Learning Techniques for Polycystic Ovary Syndrome [J]. Int J Performability Eng, 2023, 19(3): 193-202.
[14]	Shobhanam Krishna and Sumati Sidharth. AI-Powered Workforce Analytics: Maximizing Business and Employee Success through Predictive Attrition Modelling [J]. Int J Performability Eng, 2023, 19(3): 203-215.
[15]	Bhagirath, Neetu Mittal, and Sushil Kumar. Impact of Real Time Fraud Prevention on Online Resale Platform using Machine Learning and Device Fingerprint Techniques [J]. Int J Performability Eng, 2023, 19(2): 94-104.