A software defect prediction method with metric compensation based on feature selection and transfer learning

Jinfu CHEN; Xiaoli WANG; Saihua CAI; Jiaping XU; Jingyi CHEN; Haibo CHEN

doi:10.1631/FITEE.2100468

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A software defect prediction method with metric compensation based on feature selection and transfer learning

Special Feature on Intelligent Analysis for Software Data | Updated：2022-05-23

- A software defect prediction method with metric compensation based on feature selection and transfer learning
- 一种基于特征选择与迁移学习的度量补偿软件缺陷预测方法
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 5, Pages: 715-731(2022)
- Affiliations：
  
  1.School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang212013, China
  2.Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang212013, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2100468
  CLC： TP311.5
- Received：30 September 2021，
  
  Accepted：05 February 2022，
  
  Published Online：04 April 2022，
  
  Published：0 May 2022
- Accepted：
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Jinfu CHEN, Xiaoli WANG, Saihua CAI, et al. A software defect prediction method with metric compensation based on feature selection and transfer learning[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(5): 715-731.
DOI：

Jinfu CHEN, Xiaoli WANG, Saihua CAI, et al. A software defect prediction method with metric compensation based on feature selection and transfer learning[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(5): 715-731. DOI： 10.1631/FITEE.2100468.

摘要

跨项目软件缺陷预测解决了传统缺陷预测中训练数据不足的问题，克服了将多个不同源项目中学习的模型应用于目标项目的挑战。与此同时，出现两个新问题：（1）模型训练过程中过多无关和冗余特征影响训练效率，降低了模型预测精度；（2）由于开发环境等因素，度量值的分布因项目而异，当模型用于跨项目预测时，预测精度较低。本文引入皮尔逊特征选择方法解决数据冗余问题，采用基于迁移学习的度量补偿技术解决源项目和目标项目之间数据分布差异较大的问题。提出一种基于特征选择和迁移学习的度量补偿软件缺陷预测方法。实验结果表明，用该方法构建的模型在AUC（接收器工作特性曲线下面积）值和F1度量指标上取得较好结果。

Abstract

Cross-project software defect prediction solves the problem of insufficient training data for traditional defect prediction

and overcomes the challenge of applying models learned from multiple different source projects to target project. At the same time

two new problems emerge: (1) too many irrelevant and redundant features in the model training process will affect the training efficiency and thus decrease the prediction accuracy of the model; (2) the distribution of metric values will vary greatly from project to project due to the development environment and other factors

resulting in lower prediction accuracy when the model achieves cross-project prediction. In the proposed method

the Pearson feature selection method is introduced to address data redundancy

and the metric compensation based transfer learning technique is used to address the problem of large differences in data distribution between the source project and target project. In this paper

we propose a software defect prediction method with metric compensation based on feature selection and transfer learning. The experimental results show that the model constructed with this method achieves better results on area under the receiver operating characteristic curve (AUC) value and F1-measure metric.

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references

Amasaki S , Kawata K , Yokogawa T , 2015 . Improving cross-project defect prediction methods with data simplification . Proc 41 st Euromicro Conf on Software Engineering and Advanced Applications , p. 96 - 103 . doi: 10.1109/SEAA.2015.25 http://doi.org/10.1109/SEAA.2015.25

Briand LC , Melo WL , Wüst J , 2002 . Assessing the applicability of fault-proneness models across object-oriented software projects . IEEE Trans Softw Eng , 28 ( 7 ): 706 - 720 . doi: 10.1109/TSE.2002.1019484 http://doi.org/10.1109/TSE.2002.1019484

Cai JC , Xu K , Zhu YH , et al. , 2020 . Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest . Appl Energy , 262 : 114566 . doi: 10.1016/j.apenergy.2020.114566 http://doi.org/10.1016/j.apenergy.2020.114566

Chen JY , Yang YT , Hu KK , et al. , 2019 . Multiview transfer learning for software defect prediction . IEEE Access , 7 : 8901 - 8916 . doi: 10.1109/ACCESS.2018.2890733 http://doi.org/10.1109/ACCESS.2018.2890733

Chen JY , Hu KK , Yu Y , et al. , 2020 . Software visualization and deep transfer learning for effective software defect prediction . Proc ACM/IEEE 42 nd Int Conf on Software Engineering , p. 578 - 589 . doi: 10.1145/3377811.3380389 http://doi.org/10.1145/3377811.3380389

Chen X , Zhao YQ , Wang QP , et al. , 2018 . MULTI: multi-objective effort-aware just-in-time software defect prediction . Inform Softw Technol , 93 : 1 - 13 . doi: 10.1016/j.infsof.2017.08.004 http://doi.org/10.1016/j.infsof.2017.08.004

Fukushima T , Kamei Y , McIntosh S , et al. , 2014 . An empirical study of just-in-time defect prediction using cross-project models . Proc 11 th Working Conf on Mining Software Repositories , p. 172 - 181 . doi: 10.1145/2597073.2597075 http://doi.org/10.1145/2597073.2597075

Grimm LG , Jr Nesselroade KP , 2018 . Statistical Applications for the Behavioral and Social Sciences ( 2 nd Ed.). John Wiley & Sons , Hoboken, USA .

Guo YC , Shepperd M , Li N , 2018 . Bridging effort-aware prediction and strong classification: a just-in-time software defect prediction study . Proc 40 th Int Conf on Software Engineering: Companion Proceeedings , p. 325 - 326 . doi: 10.1145/3183440.3194992 http://doi.org/10.1145/3183440.3194992

Habibi PA , Amrizal V , Bahaweres RB , 2018 . Cross-project defect prediction for web application using naive Bayes (case study: petstore web application) . Proc Int Workshop on Big Data and Information Security , p. 13 - 18 . doi: 10.1109/IWBIS.2018.8471701 http://doi.org/10.1109/IWBIS.2018.8471701

Hall T , Beecham S , Bowes D , et al. , 2012 . A systematic literature review on fault prediction performance in software engineering . IEEE Trans Softw Eng , 38 ( 6 ): 1276 - 1304 . doi: 10.1109/TSE.2011.103 http://doi.org/10.1109/TSE.2011.103

He P , Li B , Liu X , et al. , 2015 . An empirical study on software defect prediction with a simplified metric set . Inform Softw Technol , 59 : 170 - 190 . doi: 10.1016/j.infsof.2014.11.006 http://doi.org/10.1016/j.infsof.2014.11.006

Herbold S , Trautsch A , Grabowski J , 2018 . A comparative study to benchmark cross-project defect prediction approaches . Proc 40 th Int Conf on Software Engineering , p. 1063 . doi: 10.1145/3180155.3182542 http://doi.org/10.1145/3180155.3182542

Iqbal T , Cao Y , Kong QQ , et al. , 2020 . Learning with out-of-distribution data for audio classification . Proc IEEE Int Conf on Acoustics, Speech and Signal Processing , p. 636 - 640 . doi: 10.1109/ICASSP40776.2020.9054444 http://doi.org/10.1109/ICASSP40776.2020.9054444

Kamei Y , Fukushima T , McIntosh S , et al. , 2016 . Studying just-in-time defect prediction using cross-project models . Empir Softw Eng , 21 ( 5 ): 2072 - 2106 . doi: 10.1007/s10664-015-9400-x http://doi.org/10.1007/s10664-015-9400-x

Li K , Xiang ZL , Chen T , et al. , 2020a . BILO-CPDP: bi-level programming for automated model discovery in cross-project defect prediction . Proc 35 th IEEE/ACM Int Conf on Automated Software Engineering , p. 573 - 584 . doi: 10.1145/3324884.3416617 http://doi.org/10.1145/3324884.3416617

Li K , Xiang ZL , Chen T , et al. , 2020b . Understanding the automated parameter optimization on transfer learning for cross-project defect prediction: an empirical study . Proc ACM/IEEE 42 nd Int Conf on Software Engineering , p. 566 - 577 . doi: 10.1145/3377811.3380360 http://doi.org/10.1145/3377811.3380360

Liu C , Yang D , Xia X , et al. , 2019 . A two-phase transfer learning model for cross-project defect prediction . Inform Softw Technol , 107 : 125 - 136 . doi: 10.1016/j.infsof.2018.11.005 http://doi.org/10.1016/j.infsof.2018.11.005

Lv WD , 2019 . Method and application of data defect analysis based on linear discriminant regression of far subspace . Cluster Comput , 22 ( 2 ): 4277 - 4282 . doi: 10.1007/s10586-018-1861-4 http://doi.org/10.1007/s10586-018-1861-4

Madeyski L , Jureczko M , 2015 . Which process metrics can significantly improve defect prediction models? An empirical study . Softw Qual J , 23 ( 3 ): 393 - 422 . doi: 10.1007/s11219-014-9241-7 http://doi.org/10.1007/s11219-014-9241-7

Malhotra R , 2015 . A systematic review of machine learning techniques for software fault prediction . Appl Soft Comput , 27 : 504 - 518 . doi: 10.1016/j.asoc.2014.11.023 http://doi.org/10.1016/j.asoc.2014.11.023

Marian Z , Mircea IG , Czibula IG , et al. , 2016 . A novel approach for software defect prediction using fuzzy decision trees . Proc 18 th Int Symp on Symbolic and Numeric Algorithms for Scientific Computing , p. 240 - 247 . doi: 10.1109/SYNASC.2016.046 http://doi.org/10.1109/SYNASC.2016.046

McBride R , Wang K , Ren ZY , et al. , 2019 . Cost-sensitive learning to rank . Proc 33 rd AAAI Conf on Artificial Intelligence , p. 4570 - 4577 . doi: 10.1609/aaai.v33i01.33014570 http://doi.org/10.1609/aaai.v33i01.33014570

Nam J , Pan SJ , Kim S , 2013 . Transfer defect learning . Proc 35 th Int Conf on Software Engineering , p. 382 - 391 . doi: 10.1109/ICSE.2013.6606584 http://doi.org/10.1109/ICSE.2013.6606584

Peng ML , Zhang Q , Xing XY , et al. , 2019 . Trainable undersampling for class-imbalance learning . Proc 33 rd AAAI Conf on Artificial Intelligence , p. 4707 - 4714 . doi: 10.1609/aaai.v33i01.33014707 http://doi.org/10.1609/aaai.v33i01.33014707

Purnami SW , Trapsilasiwi RK , 2017 . SMOTE-least square support vector machine for classification of multiclass imbalanced data . Proc 9 th Int Conf on Machine Learning and Computing , p. 107 - 111 . doi: 10.1145/3055635.3056581 http://doi.org/10.1145/3055635.3056581

Rahman F , Devanbu P , 2013 . How, and why, process metrics are better . Proc 35 th Int Conf on Software Engineering , p. 432 - 441 . doi: 10.1109/ICSE.2013.6606589 http://doi.org/10.1109/ICSE.2013.6606589

Ryu D , Choi O , Baik J , 2014 . Improving prediction robustness of VAB-SVM for cross-project defect prediction . Proc IEEE 17 th Int Conf on Computational Science and Engineering , p. 994 - 999 . doi: 10.1109/CSE.2014.198 http://doi.org/10.1109/CSE.2014.198

Ryu D , Choi O , Baik J , 2016 . Value-cognitive boosting with a support vector machine for cross-project defect prediction . Empir Softw Eng , 21 ( 1 ): 43 - 71 . doi: 10.1007/s10664-014-9346-4 http://doi.org/10.1007/s10664-014-9346-4

Ryu D , Jang JI , Baik J , 2017 . A transfer cost-sensitive boosting approach for cross-project defect prediction . Softw Qual J , 25 ( 1 ): 235 - 272 . doi: 10.1007/s11219-015-9287-1 http://doi.org/10.1007/s11219-015-9287-1

Saidi R , Bouaguel W , Essoussi N , 2019 . Hybrid feature selection method based on the genetic algorithm and Pearson correlation coefficient . In: Hassanien AE (Ed.), Machine Learning Paradigms: Theory and Application . Springer , Cham , p. 3 - 24 . doi: 10.1007/978-3-030-02357-7_1 http://doi.org/10.1007/978-3-030-02357-7_1

Shippey T , Bowes D , Hall T , 2019 . Automatically identifying code features for software defect prediction: using AST N -grams . Inform Softw Technol , 106 : 142 - 160 . doi: 10.1016/j.infsof.2018.10.001 http://doi.org/10.1016/j.infsof.2018.10.001

Shuai B , Li HF , Li MJ , et al. , 2013 . Software defect prediction using dynamic support vector machine . Proc 9 th Int Conf on Computational Intelligence and Security , p. 260 - 263 . doi: 10.1109/CIS.2013.61 http://doi.org/10.1109/CIS.2013.61

Siers MJ , Islam Z , 2015 . Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem . Inform Syst , 51 : 62 - 71 . doi: 10.1016/j.is.2015.02.006 http://doi.org/10.1016/j.is.2015.02.006

Tabassum S , Minku LL , Feng DY , et al. , 2020 . An investigation of cross-project learning in online just-in-time software defect prediction . Proc ACM/IEEE 42 nd Int Conf on Software Engineering , p. 554 - 565 . doi: 10.1145/3377811.3380403 http://doi.org/10.1145/3377811.3380403

Thejas GS , Garg R , Iyengar SS , et al. , 2021 . Metric and accuracy ranked feature inclusion: hybrids of filter and wrapper feature selection approaches . IEEE Access , 9 : 128687 - 128701 . doi: 10.1109/ACCESS.2021.3112169 http://doi.org/10.1109/ACCESS.2021.3112169

Tsuda N , Washizaki H , Honda K , et al. , 2019 . WSQF: comprehensive software quality evaluation framework and benchmark based on SQuaRE . Proc IEEE/ACM 41 st Int Conf on Software Engineering: Software Engineering in Practice , p. 312 - 321 . doi: 10.1109/ICSE-SEIP.2019.00045 http://doi.org/10.1109/ICSE-SEIP.2019.00045

Wahono RS , 2015 . A systematic literature review of software defect prediction: research trends, datasets, methods and frameworks . J Softw Eng , 1 ( 1 ): 1 - 16 .

Wan ZY , Xia X , Hassan AE , et al. , 2020 . Perceptions, expectations, and challenges in defect prediction . IEEE Trans Softw Eng , 46 ( 11 ): 1241 - 1266 . doi: 10.1109/TSE.2018.2877678 http://doi.org/10.1109/TSE.2018.2877678

Wang HJ , Khoshgoftaar TM , Napolitano A , 2010 . A comparative study of ensemble feature selection techniques for software defect prediction . Proc 9 th Int Conf on Machine Learning and Applications , p. 135 - 140 . doi: 10.1109/ICMLA.2010.27 http://doi.org/10.1109/ICMLA.2010.27

Watanabe S , Kaiya H , Kaijiri K , 2008 . Adapting a fault prediction model to allow inter languagereuse . Proc 4 th Int Workshop on Predictor Models in Software Engineering , p. 19 - 24 . doi: 10.1145/1370788.1370794 http://doi.org/10.1145/1370788.1370794

Wu F , Jing XY , Dong XW , et al. , 2017 . Cross-project and within-project semi-supervised software defect prediction problems study using a unified solution . Proc IEEE/ACM 39 th Int Conf on Software Engineering Companion , p. 195 - 197 . doi: 10.1109/ICSE-C.2017.72 http://doi.org/10.1109/ICSE-C.2017.72

Yang XL , Lo D , Xia X , et al. , 2017 . TLEL: a two-layer ensemble learning approach for just-in-time defect prediction . Inform Softw Technol , 87 : 206 - 220 . doi: 10.1016/j.infsof.2017.03.007 http://doi.org/10.1016/j.infsof.2017.03.007

Yu JL , Benesty J , Huang GP , et al. , 2015 . Optimal single-channel noise reduction filtering matrices from the Pearson correlation coefficient perspective . Proc IEEE Int Conf on Acoustics, Speech and Signal Processing , p. 201 - 205 . doi: 10.1109/ICASSP.2015.7177960 http://doi.org/10.1109/ICASSP.2015.7177960

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