计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 221200145-7.doi: 10.11896/jsjkx.221200145
王嬉, 赵春蕾, 步志亮, 杨艺
WANG Xi, ZHAO Chunlei, BU Zhiliang, YANG Yi
摘要： 针对传统强化学习算法需要行为准则学习导致测试效率偏低这一问题,提出一种基于模型的Android应用程序自动化测试方法——SA-UCB。使用Sarsa算法对测试过程进行指导,采取Q表作为动作策略的选取参照。并针对经典Sarsa算法使用的ε-greedy策略随机性过强的问题,引入上界置信算法(the Upper Confidence Bound Algorithm,UCB算法)来平衡测试过程中的“探索-利用窘境”,从而对Sarsa算法进行改进,使动作决策更加分散化,并将其应用于Android自动化测试过程,提高了测试效率。将SA-UCB方法与其他5种测试方法从测试覆盖率、测试效率、故障检测3个方面进行了测试性能的对比实验,结果表明,在相同的实验条件下,SA-UCB策略在测试覆盖率和测试效率方面具有一定优势。
中图分类号:
| [1]China Internet Network Information Center.The 49th ChinaStatistical Report on Internet Development[R].Beijing:China Internet Network Information Center,2022. [2]LIU I J,JAIN U,YEH R A,et al.Cooperative exploration formulti-agent deep reinforcement learning[C]//International Conference on Machine Learning.PMLR,2021:6826-6836. [3]BELHADI A,DJENOURI Y,SRIVASTAVA G,et al.Rein-forcement learning multi-agent system for faults diagnosis of mircoservices in industrial settings[J].Computer Communications,2021,177:213-219. [4]IBARZ J,TAN J,FINN C,et al.How to train your robot with deep reinforcement learning:lessons we have learned[J].The International Journal of Robotics Research,2021,40(4/5):698-721. [5]ZHU K,ZHANG T.Deep reinforcement learning based mobile robot navigation:A review[J].Tsinghua Science and Technology,2021,26(5):674-691. [6]ZHANG Z,LINIGER A,DAI D,et al.End-to-end urban driving by imitating a reinforcement learning coach[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:15222-15232. [7]AMALFITANO D,FASOLINO A R,TRAMONTANA P,et al.Using gui ripping for automated testing of android applications[C]//2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering.IEEE,2012:258-261. [8]AMALFITANO D,FASOLINO A R,TRAMONTANA P,et al.Mobiguitar:Automated model-based testing of mobile apps[J].IEEE Software,2014,32(5):53-59. [9]YANG W,PRASAD M R,XIE T.A grey-box approach for automated gui-model generation of mobile applications[C]//International Conference on Fundamental Approaches to Software Engineering.Springer,2013:250-265. [10]BAEK Y M,BAE D H.Automated model-based android gui tes-ting using multi-level gui comparison criteriaC]//Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering.2016:238-249. [11]SU T,MENG G,CHEN Y,et al.Guided,stochastic model-based gui testing of android apps[C]//Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering.2017:245-256. [12]LI Y,YANG Z,GUO Y,et al.Droidbot:a lightweight ui-guided test input generator for android[C]//2017 IEEE/ACM 39th International Conference on Software Engineering Companion(ICSEC).IEEE,2017:23-26. [13]CAI T,ZHANG Z,YANG P.Fastbot:A multi-agent model-based test generation system[C]//Proceedings of the IEEE/ACM 1st International Conference on Automation of Software Test.2020:93-96. [14]LV Z,PENG C,ZHANG Z,et al.Fastbot2:Reusable Automated Model-based GUI Testing for Android Enhanced by Reinforcement Learning[C]//37th IEEE/ACM International Conference on Automated Software Engineering.2022:1-5. [15]ADAMO D,KHAN M K,KOPPULA S,et al.Reinforcement learning for android gui testing[C]//Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design,Selection,and Evaluation.2018:2-8. [16]ZHOU Z.Machine Learning[M].Tsinghua University Press,2016. [17]MARIANI L,PEZZE M,RIGANELLI O.Autoblacktest:Au-tomatic black-box testing of interactive applications[C]//2012 IEEE Fifth International Conference on Software Testing,Verification and Validation.IEEE,2012:81-90. [18]KOROGLU Y,SEN A,MUSLU O.Qbe:Qlearning-based exploration of android applications[C]//2018 IEEE 11th International Conference on Software Testing,Verification and Validation(ICST).IEEE,2018:105-115. [19]YASIN H N,HAMID S H A,RAJA YUSOF R J.Droidbotx:Test case generation tool for android applications using q-lear-ning[J].Symmetry,2021,13(2):310. [20]LI Y,YANG Z,GUO Y,et al.A deep learning based approach to automated android app testing[J].arXiv:1901.02633,2019. [21]VUONG T A T,TAKADA S.Semantic analysis for deep q-network in android gui testing[C]//SEKE.2019:123-170. [22]SUTTON R S,BARTO A G.Reinforcement learning:An introduction[M].MIT Press,2018. [23]WANG Y H,LI T H S,LIN C J.Backward q-learning:Thecombination of sarsa algorithm and q-learning[J].Engineering Applications of Artificial Intelligence,2013,26(9):2184-2193. [24]GU T,CAO C,LIU T.Aimdroid:Activity-insulated multi-level automated testing for android applications[C]//2017 IEEE International Conference on Software Maintenance and Evolution(ICSME).IEEE,2017:103-114. [25]KHAN M K,BRYCE R.Android gui test generation with sarsa[C]//2022 IEEE 12th Annual Computing and Communication Workshop and Conference(CCWC).IEEE,2022:487-493. [26]CARINO S,ANDREWS J H.Dynamically testing GUIs using ant colony optimization(T)[C]//2015 30th IEEE/ACM International Conference on Automated Software Engineering(ASE).IEEE,2015:138-148. [27]ABUL O,POLAT F,ALHAJJ R.Multiagent reinforcementlearning using function approximation[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C(Applications and Reviews),2000,30(4):485-497,56. [28]CHOUDHARY S R,GORLA A,ORSO A.Automated test input generation for android:Are we there yet?[C]//2015 30th IEEE/ACM International Conference on Automated Software Engineering(ASE).IEEE,2015:429-440. [29]LAM W,WU Z,LI D.Record and replay for android:Are we there yet in industrial cases?[C]//Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering.2017:854-859. [30]APKFab.[OL].https://apkfab.com/. [31]Tencent Software Center[OL].https://pc.qq.com/. [32]Google.Ui/application exerciser monkey[EB/OL].[2022-09-26].https://developer.android.com/studio/test/monkey.html. |
|
||
首页