Computer Science ›› 2018, Vol. 45 ›› Issue (6): 161-165.doi: 10.11896/j.issn.1002-137X.2018.06.028

• Software & Database Technology • Previous Articles     Next Articles

Multi-objective Supervised Defect Prediction Modeling Method Based on Code Changes

CHEN Xiang1,2,3, WANG Qiu-ping1   

  1. School of Computer Science and Technology,Nantong University,Nantong,Jiangshu 226019,China1;
    State Key Laboratory for Novel Software Technology at Nanjing University,Nanjing 210093,China2;
    Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China3
  • Received:2017-04-24 Online:2018-06-15 Published:2018-07-24

Abstract: Defect prediction based on code changes has the advantage of smaller code inspection cost,easy fault localization and rapid fixing.This paper firstly formalized this problem as a multi-objective optimization problem.One objective is to maximize the number of identified buggy changes,and the other objective is to minimize the cost of code inspection.There exist an obvious conflict between two objectives,so this paper proposed a novel method MULTI.This me-thod can generate a set of non-dominated prediction models.In the empirical studies,this paper chose six large-scale open source projects (with 227417code changes in total) and considerd ACC and POPT as evaluation indicators of perfor-mance.Final results show that the proposed method can perform significantly better than the state-of-the-art supervised methods (i.e.,EALR and Logistic) and unsupervised methods (i.e.,LT and AGE).

Key words: Code changes, Empirical studies, Multi-objective optimization, Software defect prediction

CLC Number: 

  • TP311.5
[1]CHEN X,GU Q,LIU W S,et al.Survey of static software defect prediction[J].Journal of Software,2016,27(1):1-25.(in Chinese)
[2]MOCKUS A,WEISS D M.Predicting risk of software changes[J].Bell Labs Technical Journal,2000,5(2):169-180.
[3]KAMEI Y,SHIHAB E,ADAMS B,et al.A large-scale empirical study of just-in-time quality assurance[J].IEEE Transactions on Software Engineering,2013,39(6):757-773.
[4]YANG X,LO D,XIA X,et al.Deep learning for just-in-time defect prediction[C]//International Conference on Software Qua-lity,Reliability,and Security.2015:17-26.
[5]KIM S,JR E J W,ZHANG Y.Classifying software changes:clean or buggy?[J].IEEE Transactions on Software Enginee-ring,2008,34(2):181-196.
[6]SHIVAJI S,WHITEHEAD E J,AKELLA R,et al.reducing features to improve code change-based bug prediction[J].IEEE Transactions on Software Engineering,2013,39(4):552-569.
[7]YANG Y,ZHOU Y,LIU J,et al.Effort-aware just-in-time defect prediction:simple unsupervised models could be better than supervised models[C]//Proceedings of the International Symposium on Foundations of Software Engineering.2016,157-168.
[8]HARMAN M,MANSOURI S A,ZHANG Y.Search-based software engineering:trends,techniques and applications[J].ACM Computing Survey,2012,45(1):1-61.
[9]HARMAN M.The relationship between search based software engineering and predictive modeling[C]//International Confe-rence on Predictive Models in Software Engineering.2010:1-13.
[10]DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[11]TAN M,TAN L,DARA S,et defect prediction for imbalance data[C]//International Conference on Software Engineering.2015:99-108.
[12]BENJAMINI Y,HOCHBERG Y.controlling the false discovery rate:a practical and powerful approach to multiple testing[J].Journal of the Royal Statistical Society,Series B (Methodological),1995,57(1):289-300.
[13]ZITZLER E,THIELE L.Multiobjective evolutionary algorithms:a comparative case study and the strength pareto approach[J].IEEE Transactions on Evolutionary Computation,1999,3(4):257-271.
[14]LIU W S,CHEN X,GU Q,et al.A cluster-analysis-based feature-selection method for software defect prediction[J].SCIENCE CHINA:Information Sciences,2016,46(9):1298-1320.(in Chinese)
[15]LIU W S,CHEN X,GU Q,et al.A Noise Tolerable Feature Selection Framework for Software Defect Prediction[J].Chinese Journal of Computers,2018,41(3):506-520.(in Chinese)
[1] SUN Gang, WU Jiang-jiang, CHEN Hao, LI Jun, XU Shi-yuan. Hidden Preference-based Multi-objective Evolutionary Algorithm Based on Chebyshev Distance [J]. Computer Science, 2022, 49(6): 297-304.
[2] LI Hao-dong, HU Jie, FAN Qin-qin. Multimodal Multi-objective Optimization Based on Parallel Zoning Search and Its Application [J]. Computer Science, 2022, 49(5): 212-220.
[3] PENG Dong-yang, WANG Rui, HU Gu-yu, ZU Jia-chen, WANG Tian-feng. Fair Joint Optimization of QoE and Energy Efficiency in Caching Strategy for Videos [J]. Computer Science, 2022, 49(4): 312-320.
[4] GUAN Zheng, DENG Yang-lin, NIE Ren-can. Non-negative Matrix Factorization Based on Spectral Reconstruction Constraint for Hyperspectral and Panchromatic Image Fusion [J]. Computer Science, 2021, 48(9): 153-159.
[5] ZHENG Xiao-meng, GAO Meng, TENG Jun-yuan. Research on Construction Method of Defect Prediction Dataset for Spacecraft Software [J]. Computer Science, 2021, 48(6A): 575-580.
[6] TENG Jun-yuan, GAO Meng, ZHENG Xiao-meng, JIANG Yun-song. Noise Tolerable Feature Selection Method for Software Defect Prediction [J]. Computer Science, 2021, 48(12): 131-139.
[7] WANG Ke, QU Hua, ZHAO Ji-hong. Multi-objective Optimization Method Based on Reinforcement Learning in Multi-domain SFC Deployment [J]. Computer Science, 2021, 48(12): 324-330.
[8] CUI Guo-nan, WANG Li-song, KANG Jie-xiang, GAO Zhong-jie, WANG Hui, YIN Wei. Fuzzy Clustering Validity Index Combined with Multi-objective Optimization Algorithm and Its Application [J]. Computer Science, 2021, 48(10): 197-203.
[9] ZHU Han-qing, MA Wu-bin, ZHOU Hao-hao, WU Ya-hui, HUANG Hong-bin. Microservices User Requests Allocation Strategy Based on Improved Multi-objective Evolutionary Algorithms [J]. Computer Science, 2021, 48(10): 343-350.
[10] ZHANG Qing-qi, LIU Man-dan. Multi-objective Five-elements Cycle Optimization Algorithm for Complex Network Community Discovery [J]. Computer Science, 2020, 47(8): 284-290.
[11] ZHENG You-lian, LEI De-ming, ZHENG Qiao-xian. Novel Artificial Bee Colony Algorithm for Solving Many-objective Scheduling [J]. Computer Science, 2020, 47(7): 186-191.
[12] ZHAO Song-hui, REN Zhi-lei, JIANG He. Multi-objective Optimization Methods for Software Upgradeability Problem [J]. Computer Science, 2020, 47(6): 16-23.
[13] XIA Chun-yan, WANG Xing-ya, ZHANG Yan. Test Case Prioritization Based on Multi-objective Optimization [J]. Computer Science, 2020, 47(6): 38-43.
[14] SUN Min, CHEN Zhong-xiong, YE Qiao-nan. Workflow Scheduling Strategy Based on HEDSM Under Cloud Environment [J]. Computer Science, 2020, 47(6): 252-259.
[15] WANG Xu-liang, NIE Tie-zheng, TANG Xin-ran, HUANG Ju, LI Di, YAN Ming-sen, LIU Chang. Study on Dynamic Adaptive Caching Strategy for Streaming Data Processing [J]. Computer Science, 2020, 47(11): 122-127.
Full text



No Suggested Reading articles found!