计算机科学 ›› 2020, Vol. 47 ›› Issue (6): 1-7.doi: 10.11896/jsjkx.200400081
王慧妍, 徐经纬, 许畅
WANG Hui-yan, XU Jing-wei, XU Chang
摘要: 随着软件智能化与大数据时代的到来,环境感知自适应软件作为智能软件中的代表趋于流行。环境感知自适应软件有两大特征:1)“环境感知”,即能够通过传感器等设备感知周围环境并采集环境数据;2)“自适应”,即能够基于采集的环境数据自适应地进行软件决策。这类软件的主要表现特征为在运行时刻能够动态感知周边环境的变化并进行交互,从而做出决策。此外,随着大数据时代的到来,越来越多的人工智能模型被使用并被期望能够帮助环境感知自适应软件更好地实现自适应机制,使其能够更加智能地通过与环境的感知交互来做决策。一方面,由于运行时环境复杂,该类软件的运行时环境情况往往难以估计和预料,使得其在实际部署后运行在复杂环境中的可靠性很难通过事先测试得到有效保障,这也成为了这类软件在运行时得到有效质量保障所面临的一大挑战。而另一方面,此类软件对人工智能模型的应用与人工智能模型基于统计的核心特征,使得其在运行时刻选择应用人工智能模型来进行辅助决策也存在一定的局限性,这更加剧了保障此类软件在运行时刻质量的难度。因此,如何能够在此类软件的实际部署运行时更好地保障其运行质量与可靠性成为了当今智能软件工程的一个广泛研究的问题。与此同时,输入验证被认为是保障运行时刻软件质量的一大常用手段,它通过对软件输入进行有效识别,来避免不合适的输入在运行时刻被输入软件而影响软件行为。基于此,文中对环境感知自适应软件的运行时输入验证技术进行总结与综述,基于此类软件的两大特征,从“环境感知”方面的环境数据感知模块的输入验证及“自适应”方面决策模块的输入验证两个方面,分别对已有技术进行调研与综述。同时,文中还探讨了对环境感知自适应软件的运行时输入验证技术问题中的主要性能挑战,为实现更加高效的输入验证做框架性总结。最后,还对人工智能技术广泛应用于环境感知自适应软件的现状带来的对此类软件额外决策的挑战做了讨论与分析,已有工作对此挑战的探索也让此类软件进一步成熟,并为其未来集成决策逻辑演化从而达到软件自成长的理想提供支撑。通过对相关技术的综述,试图为相关领域的科研工作者勾画一个对环境感知自适应软件在运行时刻较清晰的质量保障框架,为未来的相关研究提供可能的方向与角度。
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[1]EI K X,CAO Y Z,YANG J F,et al.DeepXplore:Automated Whitebox Testing of Deep Learning Systems[C]//Proceedings of the 26th Symposium on Operating Systems Principles (SOSP).ACM,2017:1-18. [2]BU L,XIONG W,LIANG C J M,et al.Systematicaly Ensuring the Confidence of Real-time Home Automation IoT Systems [J].ACM Transactions on Cyber-Physical Systems,2018,2(3):1-23. [3]XU C,CHEUNG S C,MA X X,et al.ADAM:Identifying Defects in Context-aware Adaptation [J].The Journal of Systems and Software (JSS),2012,85(12):2812-2828. [4]JEFFERY S R,GAROFALAKIS M,FRANKLIN M J.Adaptive Cleaning for RFID Data Streams[C]//International Conference on Very Large Data Bases (VLDB).2006:163-174. [5]WU Z L,LI C H,NG J K Y,et al.Location Estimation via Support Vector Regression [J].IEEE Trans.on Mobile Computing (TMC),2007,6(3):311-321. [6]RAO J,DORAISWAMY S,THAKKAR H,et al.A deferred cleansing method for RFID data analytics[C]//Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB).Seoul,Korea,2006:175-186. [7]KHOUSSAINOVA N,BALAZINSKA M,SUCIU D.Towards Correcting Input Data Errors Probabilistically Using Integrity Constraints[C]//5th International ACM Workshop on Data Engineering for Wireless and Mobile Access.Chicago,Illinois,USA,2006:43-50. [8]SUBRAMANIAM S,PALPANAS T,PAPADOPOULOS D, et al.Online Outlier Detection In Sensor Data Using Non-parametric Models[C]//Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB).New York:ACM Press,2006:187-198. [9]ZHUANG Y,CHEN L.In-network outlier cleaning for data collection in sensor networks[C]//Proceedings of the 1st International VLDB Workshop on Clean Databases (CleanDB).New York:ACM Press,2006:41-48. [10]YANG W H,XU C,LIU Y P,et al.Verifying Self-adaptive Applications Suffering Uncertainty[C]//Proceedings of the 29th IEEE/ACM International Conference on Automated Software Engineering (ASE).2014:199-209. [11]JAIN A,CHANG E Y,WANGY F.Adaptive Stream Resource Management Using Kalman Filters[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD).ACM Press,2004:11-22. [12]DESHPANDE A,GUESTRIN C,MADDEN S R.Using Probabilistic Models for Data Management in Acquisitional Environments[C]//2nd Biennial Conference on Innovative Data Systems Research.Asilomar,California,USA,2005:1-13. [13]XU C,CHEUNG S C,CHAN W K,et al.Partial Constraint Checking for Context Consistency in Pervasive Computing [J].ACM Transactions on Software Engineering and Methodology (TOSEM),2010,19(3):1-61. [14]NENTWICH C,CAPRA L,EMMERICH W,et al.Xlinkit:A Consistency Checking and Smart Link Generation Service [J].ACM Transaction on Internet Technology (TOIT),2002,2(2):151-185. [15]XU C,LIU Y P,CHEUNG S C,et al.Towards Context Consistency by Concurrent Checking for Internetware Applications [J].Science China Information Sciences (SCIS),2013,56(8):1-20. [16]SUI J,XU C,CHEUNG S C,et al.Hybrid CPU-GPU Constraint Checking:Towards Efficient Context Consistency [J].Information and Software Technology (IST),2016,74:230-242. [17]WANG H Y,XU C,GUO B Y,et al.Generic Adaptive Scheduling for Efficient Context Inconsistency Detection [J].IEEE Transactions on Software Engineering (TSE),2019,PP(99):1-1. [18]INSUK P,LEE D,HYUN S J.A Dynamic Context-conflict Management Scheme for Group-aware Ubiquitous Computing Environments[C]//Proceedings of the 29th Annual InternationalComputer Software and Applications Conference.IEEE ComputerSociety,2005:359-364. [19]RANGANATHAN A,CAMPBELL R H.An infrastructure for context-awareness based on first order logic [J].Personal and Ubiquitous Computing (PUC),2003,7(6):353-364. [20]CHOMICKI J,LOBO J,NAQVI S.Conflict Resolution Using Logic Programming [J].IEEE Transactions on Knowledge and Data Engineering (TKDE),2003,15:244-249. [21]BU Y,GU T,TAO X,et al.Managing Quality of Context in Pervasive Computing[C]//Proceedings of the Sixth InternationalConference on Quality Software.IEEE Computer Society,2006:193-200. [22]XU C,CHEUNG S C,CHAN W K,et al.Heuristics-based Strategies for Resolving Context Inconsistencies in Pervasive Computing Applications[C]//Proceedings of the 28th International Conference on Distributed Computing Systems (ICDCS).2008:713-721. [23]XU C,CHEUNG S C,CHAN W K,et al.On Impact-oriented Automatic Resolution of Pervasive Context Inconsistency[C]//Proceedings of the 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE).2007:569-572. [24]XU C,MA X X,CAO C,et al.Minimizing the Side Effect of Context Inconsistency Resolution for Ubiquitous Computing[C]//Proceedings of the 8th ICST International Conference on Mobile and Ubiquitous Systems (MOBIQUITOUS).2011:285-297. [25]NUSEIBEH B,EASTERBROOK S,RUSSO A.Making Inconsistency Respectable in Software Development [J].The Journal of Systems and Software,2001,58:171-180. [26]XU C,XI W,CHEUNG S C,et al.CINA:Suppressing the Detection of Unstable Context Inconsistency [J].IEEE Transactions on Software Engineering (TSE),2015,41(9):842-865. [27]RAZ O,KOOPMAN P,SHAW M.Semantic anomaly detection in online data sources[C]//Proceedings of the 24th International Conference on Software Engineering.2002:302-312. [28]ZHENG W,LYU M,XIE T.Test selection for result inspection via mining predicate rules[C]//Proceedings of the 31st International Conference on Software Engineering.2009:215-225. [29]NADI S,BERGER T,KASTNER C,et al.Where Do Configuration Constraints Stem From An Extraction Approach and An Empirical Study [J].IEEE Transactions on Software Enginee-ring,2015,41(8):820-841. [30]XU K,TIAN K,YAO D,et al.A Sharper Sense of Self:Probabilistic Reasoning of Program Behaviors for Anomaly Detection with Context Sensitivity[C]//Proceedings of the 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).2016:467-478. [31]YANG W H,XU C,PAN M X,et al.Efficient Validation of Self-adaptive Applications by Counterexample Probability Maximization [J].The Journal of Systems and Software (JSS),2018,138:82-99. [32]QIN Y,XIE T,XU C,et al.CoMID:Context-based Multi-invariant Detection for Monitoring Cyber-physical Software [J].IEEE Transactions on Reliability (TR),2020,69(1):106-123. [33]HENDRYCKS D,GIMPEL K.A baseline for detecting misclassified and out-of-distribution examples in neural networks[C]//Proceedings of 5th International Conference on Learning Representations (ICLR).2017. [34]LIANG S,LI Y X,SRIKANT R.Principled detection of out-of-distribution examples in neural networks[C]//Proceedings of 5th International Conference on Learning Representations (ICLR).2018. [35]LEE K M,LEE K,LEE H,et al.A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks[C]//Advances in Neural Information Processing Systems (NIPS).2018:7167-7177. [36]KIM J H,FELDT R,YOO S.Guiding deep learning system testing using surprise adequacy[C]//Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE).2019. [37]ZHANG M S,ZHANG Y Q,ZHANG L M,et al.DeepRoad:GAN-based metamorphic testing and input validation framework for autonomous driving systems[C]//Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE).ACM,2018:132-142. [38]GOODFELLOW I J,SHLENS J,SZEGEDY C.Explaining and harnessing adversarial examples[C]//The 32nd International Conference on Machine Learning (ICML).2015. [39]WANG J Y,DONG G L,SUN J,et al.Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing[C]//In Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE).2019. [40]XU W L,EVANS D,QI Y.Feature Squeezing:Detecting Adversarial Examples in Deep Neural Networks[C]//Network and Distributed System Security Symposium.2018. [41]MENG D Y,CHEN H.MagNet:a Two-Pronged Defense against Adversarial Examples[C]//Proceedings of ACM Conference on Computer and Communications Security (CCS).2017. [42]FEINMAN R,CURTIN R R,SHINTRE S,et al.Detecting Adversarial Samples from Artifacts[J].arXiv:1703.00410,2017. [43]LIANG B,LI H,SU M,et al.Detecting Adversarial Examples in Deep Networks with Adaptive Noise Reduction [J].IEEE Transactions on Dependable & Secure Computing,2017,PP(99). [44]GEBHART T,SCHRATER P.Adversary Detection in Neural Networks via Persistent Homology[J].arXiv:1711.10056,2017. [45]QIN Y,WANG H Y,XU C,et al.SynEva:Evaluating ML Programs by Mirror Program Synthesis[C]//Proceedings of the International Conference on Software Quality,Reliability and Security (QRS).2018:171-182. [46]CHEN Z Q,WANG H Y,XU C,et al.Vision:Evaluating Scenario Suitableness for DNN Models by Mirror Synthesis[C]//In Proceedings of the 26th Asia-Pacific Software Engineering Conference (APSEC).IEEE Computer Society,2019:78-85. [47]WANG H Y,XU J W,XU C,et al.DISSECTOR:Input Validation for Deep Learning Applications by Crossing-layer Dissection [C] //Proceedings of the 42nd ACM/IEEE International Conference on Software Engineering (ICSE).2020. |
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