[1] FOWLER M.Refactoring:improving the design of existing code[M].[S.l.]:Addison-Wesley Professional, 2018. [2] 黄华俊, 吴海涛, 高建华, 等.消除测试代码异味对代码质量的影响分析[J].小型微型计算机系统, 2020, 41(11):2420-2426. HUANG H J, WU H T, GAO J H, et al.Measuring the impact of test smell removal on software code quality[J].Journal of Chinese Computer Systems, 2020, 41(11):2420-2426.(in Chinese) [3] PALOMBA F, PANICHELLA A, ZAIDMAN A, et al.The scent of a smell:an extensive comparison between textual and structural smells[J].IEEE Transactions on Software Engineering, 2018, 44(10):977-1000. [4] 章晓芳, 朱灿.代码坏味对软件演化影响的实证研究[J].软件学报, 2019, 30(5):1422-1437. ZHANG X F, ZHU C.Empirical study of code smell impact on software evolution[J].Journal of Software, 2019, 30(5):1422-1437.(in Chinese) [5] ABUHASSAN A, ALSHAYEB M, GHOUTI L.Software smell detection techniques:a systematic literature review[J].Journal of Software:Evolution and Process, 2021, 33(3):1-48. [6] SOBRINHO E V D P, DE LUCIA A, MAIA M D A.A systematic literature review on bad smells-5 w's:which, when, what, who, where[J].IEEE Transactions on Software Engineering, 2021, 47(1):17-66. [7] 黄子杰, 陈军华, 高建华.检测JavaScript类的内聚耦合Code Smell[J].软件学报, 2021, 32(8):2505-2521. HUANG Z J, CHEN J H, GAO J H.Detecting coupling and cohesion Code Smell of JavaScript classes[J].Journal of Software, 2021, 32(8):2505-2521.(in Chinese) [8] PECORELLI F, DI NUCCI D, DE ROOVER C, et al.A large empirical assessment of the role of data balancing in machine-learning-based code smell detection[J].Journal of Systems and Software, 2020, 169:110693. [9] 黄子杰, 陈军华, 高建华.Code Smell视角下分层Web应用失血及充血现象的量化分析[J].电子学报, 2020, 48(4):772-780. HUANG Z J, CHEN J H, GAO J H.Quantifying anemia and bloodshot of layers in Web applications from the perspective of Code Smell[J].Acta Electronica Sinica, 2020, 48(4):772-780.(in Chinese) [10] LIU H, JIN J H, XU Z F, et al.Deep learning based code smell detection[J].IEEE Transactions on Software Engineering, 2021, 47(9):1811-1837. [11] ARCELLI FONTANA F, MÄNTYLÄ M V, ZANONI M, et al.Comparing and experimenting machine learning techniques for code smell detection[J].Empirical Software Engineering, 2016, 21(3):1143-1191. [12] CARAM F L, DE OLIVEIRA RODRIGUES B R, CAMPANELLI A S, et al.Machine learning techniques for code smells detection:a systematic mapping study[J].International Journal of Software Engineering and Knowledge Engineering, 2019, 29(2):285-316. [13] BOUTAIB S, BECHIKH S, PALOMBA F, et al.Code smell detection and identification in imbalanced environ-ments[J].Expert Systems with Applications, 2021, 166:114076. [14] AGNIHOTRI M, CHUG A.Application of machine learning algorithms for code smell prediction using object-oriented software metrics[J].Journal of Statistics and Management Systems, 2020, 23(7):1159-1171. [15] GUPTA H, KUMAR L, NETI L B M.An empirical framework for code smell prediction using extreme learning machine[C]//Proceedings of the 9th Annual Information Technology, Electromechanical Engineering and Microelec-tronics Conference.Washington D.C., USA:IEEE Press, 2019:189-195. [16] PECORELLI F, PALOMBA F, KHOMH F, et al.Developer-driven code smell prioritization[C]//Proceedings of the 17th International Conference on Mining Software Repositories.Washington D.C., USA:IEEE Press, 2020:220-231. [17] JAIN S, SAHA A J.Rank-based univariate feature selection methods on machine learning classifiers for code smell detection[J].Evolutionary Intelligence, 2022, 15(1):609-638. [18] DI NUCCI D, PALOMBA F, TAMBURRI D A, et al.Detecting code smells using machine learning techniques:are we there yet?[C]//Proceedings of IEEE International Conference on Software Analysis, Evolution and Reengineering.Washington D.C., USA:IEEE Press, 2018:612-621. [19] PALOMBA F, TAMBURRI D A.Predicting the emergence of community smells using socio-technical metrics:a machine-learning approach[J].Journal of Systems and Software, 2021, 171:110847. [20] KHOMH F, PENTA M D, GUÉHÉNEUC Y G, et al.An exploratory study of the impact of antipatterns on class change and fault-proneness[J].Empirical Software Engineering, 2012, 17(3):243-275. [21] BIGONHA M A S, FERREIRA K, SOUZA P, et al.The usefulness of software metric thresholds for detection of bad smells and fault prediction[J].Information and Software Technology, 2019, 115:79-92. [22] KAUR S, MAINI R.Analysis of various software metrics used to detect bad smells[J].The International Journal of Engineering and Science, 2016, 5(6):14-20. [23] PECORELLI F, PALOMBA F, NUCCI D D, et al.Comparing heuristic and machine learning approaches for metric-based code smell detection[C]//Proceedings of the 27th International Conference on Program Comprehension.Washington D.C., USA:IEEE Press, 2019:93-104. [24] DANPHITSANUPHAN P, SUWANTADA T.Code smell detecting tool and code smell-structure bug relationship[C]//Proceedings of Spring Congress on Engineering and Technology.Washington D.C., USA:IEEE Press, 2012:1-5. [25] BOLÓN-CANEDO V, ALONSO-BETANZOS A.Ensembles for feature selection:a review and future trends[J].Information Fusion, 2019, 52:1-12. [26] ROBNIK-SIKONJA M, KONONENKO I.Theoretical and empirical analysis of ReliefF and RReliefF[J].Machine Learning, 2003, 53(1/2):23-69. [27] XU H H, DENG Y.Dependent evidence combination based on shearman coefficient and Pearson coefficient[J].IEEE Access, 2018, 6:11634-11640. [28] CHEN T Q, GUESTRIN C.XGBoost:a scalable tree Boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York, USA:ACM Press, 2016:785-794. [29] 李占山, 刘兆赓.基于XGBoost的特征选择算法[J].通信学报, 2019, 40(10):101-108. LI Z S, LIU Z G.Feature selection algorithm based on XGBoost[J].Journal on Communications, 2019, 40(10):101-108.(in Chinese) [30] NGUYEN H A T, HA LE T, BUI T D.A Stacking ensemble learning model for mental state recognition towards implementation of brain computer interface[C]//Proceedings of the 6th NAFOSTED Conference on Information and Computer Science.Washington D.C., USA:IEEE Press, 2019:39-43. [31] WOLPERT D H.Stacked generalization[J].Neural Networks, 1992, 5(2):241-259. [32] AMORIM L, COSTA E, ANTUNES N, et al.Experience report:evaluating the effectiveness of decision trees for detecting code smells[C]//Proceedings of IEEE International Symposium on Software Reliability Engineering.Washington D.C., USA:IEEE Press, 2015:261-269. [33] FERENC R, TÓTH Z, LADÁNYI G, et al.A public unified bug dataset for Java[C]//Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering.Washington D.C., USA:IEEE Press, 2018:12-21. [34] FENG Y, WANG D J, YIN Y Q, et al.An XGBoost-based casualty prediction method for terrorist attacks[J].Complex & Intelligent Systems, 2020, 6(3):721-740. [35] YANG F, MAO K Z.Robust feature selection for microarray data based on multicriterion fusion[J].IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011, 8(4):1080-1092. [36] TANG J J, LIANG J, HAN C Y, et al.Crash injury severity analysis using a two-layer Stacking framework[J].Accident, Analysis and Prevention, 2019, 122:226-238. [37] MHAWISH M Y, GUPTA M.Predicting code smells and analysis of predictions:using machine learning techniques and software metrics[J].Journal of Computer Science and Technology, 2020, 35(6):1428-1445. [38] 杨荣新, 孙朝云, 徐磊.基于Stacking模型融合的光伏发电功率预测[J].计算机系统应用, 2020, 29(5):36-45. YANG R X, SUN Z Y, XU L.Photovoltaic power prediction based on Stacking model fusion[J].Computer Systems & Applications, 2020, 29(5):36-45.(in Chinese) [39] HADJ-KACEM M, BOUASSIDA N.A hybrid approach to detect code smells using deep learning[C]//Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering.Washington D.C., USA:IEEE Press, 2018:137-146. [40] CHEN H, REN Z L, QIAO L, et al.AdaBoost-based refused bequest code smell detection with synthetic instances[C]//Proceedings of the 7th International Conference on Dependable Systems and Their Applications.Washington D.C., USA:IEEE Press, 2020:78-89. |