[1] 王青, 伍书剑, 李明树. 软件缺陷预测技术[J]. 软件学报, 2008, 19(7): 1565-1580.
[2] 陈翔, 顾庆, 刘望舒, 等. 静态软件缺陷预测方法研究[J]. 软件学报, 2016, 27(1): 1-25.
[3] 陈翔, 王莉萍, 顾庆, 等. 跨项目软件缺陷预测方法研究综述[J]. 计算机学报, 2018, 41(1): 254-274.
[4] LESSMANN S, BAESENS B, MUES C, et al. Benchmarking classification models for software defect prediction: A proposed framework and novel findings[J]. IEEE Transactions on Software Engineering, 2008, 34(4): 485-496.
[5] ZIMMERMANN T, NAGAPPAN N. Predicting defects using network analysis on dependency graphs[C]//Proceedings of the 30th International Conference on Software Engineering, Leipzig Germany, May 10-18, 2008. New York: ACM, 2008: 531-540.
[6] LEE T, NAM J, HAN D, et al. Micro interaction metrics for defect prediction[C]//Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, Szeged, Hungary, September 5-9, 2011. New York: ACM, 2011: 311-321.
[7] SUN Z, SONG Q, ZHU X. Using coding-based ensemble learning to improve software defect prediction[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(6): 1806-1817.
[8] JING X Y, YING S, ZHANG Z W, et al. Dictionary learning based software defect prediction[C]//Proceedings of the 36th International Conference on Software Engineering, Hyderabad India, May 3-June 7, 2014. New York: ACM, 2014: 414-423.
[9] ZHANG J, WU J, CHEN C, et al. Cds: A cross–version software defect prediction model with data selection[J]. IEEE Access, 2020, 8: 110059-110072.
[10] 吴方君. 静态软件缺陷预测研究进展[J]. 计算机科学与探索, 2019, 13(10): 1621-1637.
[11] TANTITHAMTHAVORN C. Towards a better understanding of the impact of experimental components on defect prediction modelling[C]//Proceedings of the 38th International Conference on Software Engineering Companion, Austin, Texas, May 14-22, 2016. New York: ACM, 2016: 867-870.
[12] FU W, MENZIES T, SHEN X. Tuning for software analytics: Is it really necessary?[J]. Information and Software Technology, 2016, 76: 135-146.
[13] EIBE I W, WITTEN I H, FRANK E, et al. Weka: Practical machine learning tools and techniques with java implementations[J]. ACM Sigmod Record, 1999, 31(1): 76-77.
[14] HIGHAM D J, HIGHAM N J. MATLAB guide[M]. USA: Society for Industrial and Applied Mathematics, 2016.
[15] TOSUN A, BENER A. Reducing false alarms in software defect prediction by decision threshold optimization[C]// Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement, Florida, U.S.A, Oct 15-16, 2009. Washington: IEEE Computer Society, 2009: 477-480.
[16] JIANG Y, CUKIC B, MENZIES T. Can data transformation help in the detection of fault-prone modules?[C]//Proceedings of the 2008 Workshop on Defects in Large Software Systems, Seattle, Washington, July 20, 2008. New York: ACM, 2008: 16-20.
[17] YU Q, ZHU Y, HAN H, et al. Evolutionary measures for object-oriented projects and impact on the performance of cross-version defect prediction[C]//Proceedings of the Asia-Pacific Symposium on Internetware, Hohhot, China, June 11-12, 2022. New York: ACM, 2022: 192-201.
[18] TANTITHAMTHAVORN C, MCINTOSH S, HASSAN A E, et al. Automated parameter optimization of classification techniques for defect prediction models[C]//Proceedings of the 38th International Conference on Software Engineering, Austin, Texas, May 14-22, 2016. New York: ACM, 2016: 321-332.
[19] LI K, XIANG Z, CHEN T, et al. Understanding the automated parameter optimization on transfer learning for cross-project defect prediction: an empirical study[C]//Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, South Korea, June 27-July 19, 2020. New York: ACM, 2020: 566-577.
[20] YANG L, SHAMI A. On hyperparameter optimization of machine learning algorithms: Theory and practice[J]. Neurocomputing, 2020, 415: 295-316.
[21] HERTEL L, BALDI P, GILLEN D L. Reproducible hyperparameter optimization[J]. Journal of Computational and Graphical Statistics, 2022, 31(1): 84-99.
[22] ZHANG B, RAJAN R, PINEDA L, et al. On the importance of hyperparameter optimization for model-based reinforcement learning[C]//Proceedings of the International Conference on Artificial Intelligence and Statistics, Virtual Event, April 13-15, 2021. New York: PMLR, 2021: 4015-4023.
[23] TANTITHAMTHAVORN C, MCINTOSH S, HASSAN A E, et al. The impact of automated parameter optimization on defect prediction models[J]. IEEE Transactions on Software Engineering, 2018, 45(7): 683-711.
[24] QU Y, CHEN X, ZHAO Y, et al. Impact of Hyper Parameter Optimization for Cross-Project Software Defect Prediction[J]. International Journal of Performability Engineering, 2018, 14(6): 1291-1299.
[25] PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al. scikit-learn: Machine learning in python[J]. Journal of Machine Learning Research, 2011, 12: 2825-2830.
[26] QUINLAN J R. Induction of decision trees[J]. Machine Learning, 1986, 1(1): 81-106.
[27] COVER T, HART P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27.
[28] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[29] NOBLE W S. What is a support vector machine?[J]. Nature Biotechnology, 2006, 24(12): 1565-1567.
[30] ROSENBLATT F. Principles of neurodynamics. perceptrons and the theory of brain mechanisms[R]. New York: Cornell Aeronautical Lab Inc Buffalo, 1961.
[31] VICTORIA A H, MARAGATHAM G. Automatic tuning of hyperparameters using bayesian optimization[J]. Evolving Systems, 2021, 12(1): 217-223.
[32] BERGSTRA J, BARDENET R, BENGIO Y, et al. Algorithms for hyper-Parameter optimization[J]. Advances in Neural Information Processing Systems, 2011: 2546-2554.
[33] SHLESINGER M F. Random searching[J]. Journal of Physics A-Mathematical and Theoretical, 2009, 42(43): 434001.
[34] VAN P, AARTS E. Simulated annealing: Theory and applications[M]. Norwell, MA, U.S.A.: Dordrecht Boston, 1987.
[35] BERGSTRA J, YAMINS D, COX D D. Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms[C]//Proceedings of the 12th Python in Science Conference, Austin, Texas, June 24-29, 2013. Austin Texas: Citeseer, 2013, 13: 20.
[36] BORYSSENKO A, HERSCOVICI N. Machine learning for multiobjective evolutionary optimization in python for EM problems[C]//Proceedings of the 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Boston, U.S.A, July 8–13, 2018. Piscataway: IEEE, 2018: 541-542
[37] LINDAUER M, EGGENSPERGER K, FEURER M, et al. SMAC3: A versatile bayesian optimization package for hyperparameter optimization[J]. Jorunal of Machine Learning Research, 2022, 23(54):1-9.
[38] JURECZKO M, MADEYSKI L. Towards identifying software project clusters with regard to defect prediction[C]// Proceedings of the 6th International Conference on Predictive Models in Software Engineering, Timisoara, Romania, September 12-13, 2010. New York: ACM, 2010:1-10.
[39] HUANG J, LING C X. Using AUC and accuracy in evaluating learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(3): 299-310.
[40] TAHERI S, HESAMIAN G. A generalization of the Wilcoxon signed-rank test and its applications[J]. Statistical Papers, 2013, 54(2): 457-470.
[41] HALSEY L G, CURRAN-EVERETT D, VOWLER S L, et al. The fickle P value generates irreproducible results[J]. Nature Methods, 2015, 12(3): 179-185.
[42] DOMINGOS P. A unified bias-variance decomposition for zero-one and squared loss[J]. Association for the Advancement of Artificial Intelligence, 2000: 564-569.
[43] AOTANI T, KOBAYASHI T, SUGIMOTO K. Meta-optimization of bias-variance trade-off in stochastic model learning[J]. IEEE Access, 2021, 9: 148783-148799.
[44] OSMAN H, GHAFARI M, NIERSTRASZ O, et al. An extensive analysis of efficient bug prediction configurations[C]// Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering, Toronto, Canada, November 8, 2017. New York: ACM, 2017: 107-116.
[45] SHEPPERD M, BOWES D, HALL T. Researcher bias: The use of machine learning in software defect prediction[J]. IEEE Transactions on Software Engineering, 2014, 40(6): 603-616.
[46] MYRTVEIT I, STENSRUD E, SHEPPERD M. Reliability and validity in comparative studies of software prediction models[J]. IEEE Transactions on Software Engineering, 2005, 31(5): 380-391.
[47] SHEN L, LIU W, CHEN X, et al. Improving machine learning-based code smell detection via hyper-parameter optimization[C]// Proceedings of the 27th Asia-Pacific Software Engineering Conference (APSEC), Singapore, December 1-4, 2020. Singapore: IEEE, 2020: 276-285.
[48] GONG L, JIANG S, WANG R, et al. Empirical evaluation of the impact of class overlap on software defect prediction[C]// Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), San Diego, U.S.A, November 11-15, 2019. Piscataway: IEEE, 2019: 698-709.
参考文献
[1] 杨锐,黄海生,刘宇朝.基于ARM软核的智能家居无线传感器网络设计[J].信息技术,2020,44(5): 6-9.
[2] 潘胜民. 基于Amba总线协议的增强型DMA控制器的设计[D]. 南京: 东南大学, 2016.
[3] 蔡艳辉,胡锐,程鹏飞,等.一种基于FPGA+DSP的北斗兼容型高精度接收机系统设计[J].导航定位学报, 2013,2(2): 1-6.
[4] 赵东升.基于ARM Cortex-M3核MCU的设计与应用[D].济南:山东大学,2021.
[5] 唐守龙,刘昊,陆生礼.浅谈SoC设计中的软硬件协同设计技术[J].电子器件,2002,25(2):4.
[6] 颜伟成,陈朝阳,沈绪榜. AMBA-AHB总线接口的设计与实现[J].计算机与数字工程,2005(10):130-132.
[7] 严超.全球定位基带处理SoC设计与关键技术研究[D].重庆:重庆大学,2014.
[8] 田泽,张怡浩,于敦山,等. SoC片上总线综述[J].半导体技术,2003(11):11-15.
[9] 杨雪梅,黄海生,李鑫.基于AMBA总线的SOC中UART电路的设计与实现[J].信息技术,2019,43(6):149-152.
[10] 于乐,王嘉良.易于移植的FPGA在线更新控制器设计[J].航空电子技术,2015,46(4):47-50.
[11] 王一楠,林涛,余宁梅.基于AMBA的AHB总线矩阵设计[J].微电子学与计算机,2019,36(2):73-77.
[12]马秦生,魏翠,孙力军,等.嵌入式SoC总线分析与研究[J].中国集成电路,2007(3):.45-49.
参考文献
[1] 班多晗,吕鑫,王鑫元.基于一维混沌映射的高效图像加密算法[J].计算机科学,2020,47(4):278-284.
[2] FRIDRICH J. Symmetric ciphers based on two-dimensional chaotic maps[J].International Journal of Bifurcation and Chaos,1998, 8:1259-1284.
[3] CHEN G, MAO Y, CHUI C K. A symmetric image encryption scheme based on 3D chaotic cat maps[J]. Chaos, Solitons and Fractals,2004, 21(3):749-761.
[4] HUA Z Y, ZHOU Y C, HUANG H J. Cosine-transformbased chaotic system for image encryption[J]. Information Sciences, 2019, 480:403419.
[5] PAK C, HUANG L. A new color image encryption using combination of the 1D chaotic map[J]. Signal Processing, 2017,138:129-137.
[6] HUANG X L,YE G D. An efficient self-adaptive model for chaotic image encryption algorithm[J]. Communications in Nonlinear Science and Numerical Simulation, 2014,19(12):4094-4104.
[7] 陈志刚,梁涤青,邓小鸿,等.Logistic混沌映射性能分析与改进[J].电子与信息学报,2016,38(6):1547-1551.
[8] 刘旭.基于深度学习对一类混沌图像加密算法进行安全性分析[D].南京:南京邮电大学,2019.
[9] 刘杨.混沌伪随机序列算法及图像加密技术研究[D].哈尔滨:哈尔滨工业大学, 2015.
[10] 郝柏林.从抛物线谈起—混沌动力学引论[M].上海:上海科学技术出版社,1995:48-50.
[11] 徐红梅,郭树旭.基于符号相对熵的Logistic混沌系统时间不可逆性分析[J].电子与信息学报,2014,36(5):1242-1246.
[12] ZHENG P, MU C L, HU X, et al. Boundedness of solutions in a chemotaxis system with nonlinear sensitivity and logistic source[J]. Journal of Mathematical Analysis and Applications,2015, 424(1):509-522.
[13] 王晴,李涛,王常磊,等.一种基于Logistic映射和随机噪声的语音加密方法[J].黑龙江大学自然科学学报,2020,37(2):240-246.
[14] 贾晓霞.混沌映射在数据加密中的应用[J].电子技术与软件工程,2020(22):235-236.
[15] MAY R M. Simple mathematical models with very complicated dynamics[J].Nature, 1976, 261(5560):459-67.
[16] 杨永波,李栋.基于Logistic映射与矩阵像素置乱加密算法研究[J].现代电子技术,2022,45(16):39-144.