Computer Science ›› 2020, Vol. 47 ›› Issue (6): 1-7.doi: 10.11896/jsjkx.200400081

• Intelligent Software Engineering • Previous Articles     Next Articles

Survey on Runtime Input Validation for Context-aware Adaptive Software

WANG Hui-yan, XU Jing-wei, XU Chang   

  1. State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China
    Department of Computer Science and Technology,Nanjing University,Nanjing 210023,China
  • Received:2020-03-18 Online:2020-06-15 Published:2020-06-10
  • About author:WANG Hui-yan,born in 1993,postgra-duate,is a member of China Computer Federation.Her main research interests include context management,input validation,and intelligent software testing and analysis.
    XU Chang,born in 1977,Ph.D,professor,is a senior member of China Computer Federation.His main research interests include big data software engineering,and intelligent software testing and analysis.
  • Supported by:
    This work was supported by the Key Program of the National Natural Science Foundation of China (61932021,61802170)

Abstract: With the widespread of intelligence and big data,context-aware adaptive software,one representative of intelligent software,has gained increasing popularities.It has two key characteristics:1) “context-aware”,referring to the ability of becoming aware of environments through ubiquitous sensors.2) “adaptive”,referring to the ability of making adaptations based on collected contexts.As such,context-aware adaptive software can at runtime sense its surrounding environment and make adaptations smartly.Besides,with the growing development of artificial intelligence (AI) technologies,more AI models have been applied in context-aware adaptive software for smarter adaptations.Therefore,on one hand,due to the complexity of environments at runti-me,the software suffers from severe reliability issues during its deployment,which is difficult to avoid by sole testing due to the lack of practically controllable environments,thus leading to great challenges for its runtime reliability assurance.On the other hand,the application of AI models in context-aware adaptive software further aggravates its reliability issues.As such,how to maintain the runtime reliability of context-aware adaptive software has been a widely-open research problem in intelligent software engineering,while input validation has shown promising in this field by identifying and isolating unexpected inputs from being fed into the software in order to avoid possible uncontrollable consequences at runtime.In this article,we survey techniques on runtime input validation for context-aware adaptive software concerning its two key characteristics:“context-aware” and “adaptive”.Meanwhile,we also dig into the reliability issue problem concerning its cost-effectiveness in solving,and overview the concerned research framework.Finally,we discuss some latest concerns of context-aware adaptive software at present and in future,and present how context-aware adaptive software supports the emergence of self-growing software in the vision.As a summary,we survey existing efforts on runtime input validation for context-aware adaptive software,and aim to form a structured framework for the potential solutions on its reliability issues.We wish this may shed some light on relative researchers in future.

Key words: Context-aware adaptive applications, Runtime quality assurance, Input validation, Software reliability, Constraint checking

CLC Number: 

  • TP311
[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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