Active Learning Empirical Research on Cross-Version Software Defect Prediction Datasets

doi:10.23940/ijpe.20.04.p12.609617

Abstract

Abstract:

Software quality plays an important part in software engineering. Active learning is introduced to conduct supervised learning classifier because labeling cost is very high. However, in the real software quality assurance process, there are fewer labeled instances in the initial stage of software development, and there may be a historical data set developed by the same team. Therefore, learning from the historical data set can be used for an active learning query strategy. In our empirical study, we design and conduct experiments on promise datasets, which are gathered from real open-source projects. We find that the meta active learning query strategy can perform better than the commonly used query strategy when a little data is labeled.

Key words: active learning, cross-version software defect prediction, random forest

Fang Li, Yubin Qu, Junxia Ji, Dejun Zhang, and Long Li. Active Learning Empirical Research on Cross-Version Software Defect Prediction Datasets [J]. Int J Performability Eng, 2020, 16(4): 609-617.

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

References 26.

1.	X. Chen, Q. Gu, W. S. Liu, S. L. Liu,C. Ni, “State-of-the-Art Survey of Static Software Defect Prediction,” Journal of Software, Vol. 27, No. 1, pp. 1-25, 2016
2.	Y. B.Qu and X. Chen, “Software Defect Prediction Method based on Cost-Sensitive Active Learning,” Journal of Nantong University (Natural Science Edition), Vol. 18, No. 1, pp. 9-25, 2019
3.	S. Hosseini, B. Turhan,D. Gunarathna, “A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction,” IEEE Transactions on Software Engineering, Vol. 45, No. 2, pp. 111-147, February 2019
4.	H. H.Lu and B. Cukic, “An Adaptive Approach with Active Learning in Software Fault Prediction,” inProceedings of the 8th International Conference on Predictive Models in Software Engineering, pp. 79-88, 2012
5.	M. Li, H. Y. Zhang, R. X. Wu,Z. H. Zhou, “Sample-based Software Defect Prediction with Active and Semi-Supervised Learning,” Automated Software Engineering, Vol. 19, No. 2, pp. 201-230, June 2012
6.	G. C. Luo, Y. Ma,K. Qin, “Active Learning for Software Defect Prediction,” IEICE Transactions on Information and Systems, Vol. 95, No. 6, pp. 1680-1683, 2012
7.	H. Lu, E. Kocaguneli,B. Cukic, “Defect Prediction Between Software Versions with Active Learning and Dimensionality Reduction,” inProceedings of 2014 IEEE 25th International Symposium on Software Reliability Engineering, pp. 312-322, November 2014
8.	Z. Xu, J. Liu, X. P. Luo,T. Zhang, “Cross-Version Defect Prediction via Hybrid Active Learning with Kernel Principal Component Analysis,” inProceedings of SANER'18, pp. 209-220, IEEE, March 2018
9.	R. Malhotra and S. Kamal, “An Empirical Study to Investigate Oversampling Methods for Improving Software Defect Prediction using Imbalanced Data,” Neurocomputing, Vol. 343, pp. 120-140, May 2019
10.	J. Zhu, H. Wang,E. Hovy, “Learning a Stopping Criterion for Active Learning for Word Sense Disambiguation and Text Classification,” inProceedings of the Third International Joint Conference on Natural Language Processing, pp. 366-372, 2008
11.	K. Tomanek and U. Hahn, “Reducing Class Imbalance During Active Learning for Named Entity Annotation,” inProceedings of the Fifth International Conference on Knowledge Capture, pp. 105-112, Redondo Beach, California, USA, 2009
12.	S. Huda, K. Liu, M. Abdelrazek, A. Ibrahim, S. Alyahya, H. Al-Dossari, et al., “An Ensemble Oversampling Model for Class Imbalance Problem in Software Defect Prediction,” IEEE Access, Vol. 6, pp. 24184-24195, March 2018
13.	K. Konyushkova, R. Sznitman,P. Fua., “Learning Active Learning from Data,” inProceedings of Advances in Neural Information Processing Systems, pp. 4228-4238, Long Beach, 2017
14.	D. Lewis and W. Gale, “A Sequential Algorithm for Training Text Classifiers,” inProceedings of SIGIR Conference on Research and Development in Information Retrieval, pp. 3-12, 1994
15.	B. Settles, “Active Learning Literature Survey,” University of Wisconsin-Madison, Madison, WI, USA, 2010
16.	P. Donmez, J. G. Carbonell,P. N. Bennett, “Dual Strategy Active Learning,” inProceedings of European Conference on Machine Learning, pp. 116-127, 2007
17.	S. J. Huang, R. Jin,Z. H. Zhou, “Active Learning by Querying Informative and Representative Examples,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 10, pp. 1936 - 1949, October 2014
18.	E. Arisholm and L. C. Briand, “Predicting Fault-Prone Components in a Java Legacy System,” inProceedings of ISESE 2006, pp. 1-10, 2006
19.	A. Monden, T. Hayashi, S. Shinoda, K. Shirai, J. Yoshida, M. Barker, et al., “Assessing the Cost Effectiveness of Fault Prediction in Acceptance Testing,” IEEE Transactions on Software Engineering, Vol. 39, No. 10, pp. 1345-1357, 2013
20.	Y. Zhao, Y. Yang, H. Lu, J. Liu, H. Leung, Y. Wu, et al., “Understanding the Value of Considering Client Usage Context in Package Cohesion for Fault-Proneness Prediction,” Automated Software Engineering, Vol. 24, No. 2, pp. 393-453, 2017
21.	M. Harman, S. Islam, Y. Jia, L. L. Minku, F. Sarro,K. Srivisut, “Less is More: Temporal Fault Predictive Performance over Multiple Hadoop Releases,” inProceedings of SSBS'14, pp. 240-246, Springer, 2014
22.	B. Turhan, T. Menzies, A. B. Bener,J. D. Stefano, “On the Relative Value of Cross-Company and Within-Company Data for Defect Prediction,”Empirical Software Engineering, Vol. 14, pp. 540-578, 2009
23.	F. Peters, T. Menzies,A. Marcus, “Better Cross Company Defect Prediction,” inProceedings of 10th Working Conference on Mining Software Repositories (MSR), pp. 409-418, May 2013
24.	S. Dasgupta and D. Hsu, “Hierarchical Sampling for Active Learning,” inProceedings of the 25th International Conference on Machine Learning, pp. 208-215, ACM, 2008
25.	G. C. Luo, Y. Ma, K. Qin, “Active Learning for Software Defect Prediction,” IEICE Transactions on Information and Systems, Vol. 95, No. 6, pp. 1680-1683, 2012
26.	H. Lu, E. Kocaguneli,B. Cukic, “Defect Prediction Between Software Versions with Active Learning and Dimensionality Reduction,” inProceedings of the 25th International Symposium on Software Reliability Engineering, pp. 312-322, 2014

[1]	Sanjay M, Deepashree P. Vaideeswar, Kalapraveen Bagadi, Visalakshi Annepu, and Beebi Naseeba. Hyperspectral Image Classification: A Hybrid Approach Integrating Random Forest Feature Selection and Convolutional Neural Networks for Enhanced Accuracy [J]. Int J Performability Eng, 2024, 20(5): 263-270.
[2]	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.
[3]	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.
[4]	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.
[5]	Priyanshu Verma, Ishan Sharma, Sonia Deshmukh, and Rohit Vashisht. Customer Churn Analysis using Spark and Hadoop [J]. Int J Performability Eng, 2023, 19(10): 663-675.
[6]	Shobhanam Krishna and Sumati Sidharth. HR Analytics: Employee Attrition Analysis using Random Forest [J]. Int J Performability Eng, 2022, 18(4): 275-281.
[7]	Lakshmi Kala Pampana, and Manjula Sri Rayudu. Multi-Class Classification of Retinal Abnormality using Machine Learning Algorithms [J]. Int J Performability Eng, 2022, 18(11): 826-832.
[8]	C Chandana and G Parthasarathy. Efficient Machine Learning Regression Algorithm using Naïve Bayes Classifier for Crop Yield Prediction and Optimal Utilization of Fertilizer [J]. Int J Performability Eng, 2022, 18(1): 47-55.
[9]	Tianhao Wang, Peng Chen, Tianjiazhi Bao, Jiaheng Li, and Xiaosheng Yu. Arrhythmia Classification Algorithm based on SMOTE and Feature Selection [J]. Int J Performability Eng, 2021, 17(3): 263-275.
[10]	Kavitha Ravindran, Srinivasan Rajkumar, and Kavitha Muthuvel. An Investigation on Cervical Cancer with Image Processing and Hybrid Classification [J]. Int J Performability Eng, 2021, 17(11): 918-925.
[11]	Jiali Yang, Jiarui Chen, and Sheng Li. Decomposition and Identification of Non-Intrusive Load Equipment Group [J]. Int J Performability Eng, 2021, 17(1): 74-84.
[12]	Yubin Qu, Fang Li, and Xiang Chen. LAL: Meta-Active Learning-based Software Defect Prediction [J]. Int J Performability Eng, 2020, 16(2): 203-213.
[13]	Wenjie Li. Imbalanced Data Optimization Combining K-Means and SMOTE [J]. Int J Performability Eng, 2019, 15(8): 2173-2181.
[14]	Xiao Chen, Li Han, Meng Leng, and Xiao Pan. Similarity based on the Importance of Common Features in Random Forest [J]. Int J Performability Eng, 2019, 15(4): 1171-1180.
[15]	Yubin Qu, Xiang Chen, Ruijie Chen, Xiaolin Ju, and Jiangfeng Guo. Active Learning using Uncertainty Sampling and Query-by-Committee for Software Defect Prediction [J]. Int J Performability Eng, 2019, 15(10): 2701-2708.