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05-11-2021

Deep Learning System and It’s Automatic Testing: An Approach

Author: Rijwan Khan

Published in: Annals of Data Science | Issue 4/2023

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Abstract

The process of testing conventional programs is quite easy as compared to the programs using Deep Learning approach. The term Deep learning (DL) is used for a novel programming approach that is highly data centric and where the governing rules and logic are primarily dependent on the data used for training. Conventionally, Deep Learning models are evaluated by using a test dataset to evaluate their performance against set parameters. The difference in data and logic handling between programs using conventional methods and programs using the DL approach makes it difficult to apply the traditional approaches of testing directly to DL based programs. The accuracy of test data is currently the best measure of the adequacy of testing in the DL based systems. This poses a problem because of the difficulty in availability of test data that is of sufficient quality. This in turn restricts the level of confidence that can be established on the adequacy of testing of DL based systems. Unlike conventional applications, using the conventional programming approaches the lack of quality test data and the lack of interpretability makes the system analysis and detection of defects a difficult task in DL based systems. So testing of DL based models can be done automatically with a different approach compared to normal software.

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Literature
5.
go back to reference Takanen A, Demott JD, Miller C, Kettunen A (2018) Fuzzing for software security testing and quality assurance. Artech House Takanen A, Demott JD, Miller C, Kettunen A (2018) Fuzzing for software security testing and quality assurance. Artech House
9.
go back to reference Kim Y, Hong S, Ko B, Phan DL, Kim M (2018) Invasive software testing: mutating target programs to diversify test exploration for high test coverage. In: 2018 IEEE 11th international conference on software testing, verification and validation (ICST), pp 239–249. https://doi.org/10.1109/ICST.2018.00032 Kim Y, Hong S, Ko B, Phan DL, Kim M (2018) Invasive software testing: mutating target programs to diversify test exploration for high test coverage. In: 2018 IEEE 11th international conference on software testing, verification and validation (ICST), pp 239–249. https://​doi.​org/​10.​1109/​ICST.​2018.​00032
10.
12.
go back to reference Drave I, Hillemacher S, Greifenberg T, Rumpe B, Wortmann A, Markthaler M, Kriebel S (2018) Model-based testing of software-based system functions. In: 2018 44th Euromicro conference on software engineering and advanced applications (SEAA), pp 146–153. https://doi.org/10.1109/SEAA.2018.00032 Drave I, Hillemacher S, Greifenberg T, Rumpe B, Wortmann A, Markthaler M, Kriebel S (2018) Model-based testing of software-based system functions. In: 2018 44th Euromicro conference on software engineering and advanced applications (SEAA), pp 146–153. https://​doi.​org/​10.​1109/​SEAA.​2018.​00032
16.
go back to reference Alghamdi AM, Eassa FE (2019) Software testing techniques for parallel systems: a survey. Int J Comput Sci Netw Secur 19(4):176–186 Alghamdi AM, Eassa FE (2019) Software testing techniques for parallel systems: a survey. Int J Comput Sci Netw Secur 19(4):176–186
18.
go back to reference Markthaler M, Kriebel S, Salman KS, Greifenberg T, Hillemacher S, Rumpe B, Richenhagen J (2018) Improving model- based testing in automotive software engineering. In: 2018 IEEE/ACM 40th international conference on software engineering: software engineering in practice track (ICSE-SEIP), pp 172–180 Markthaler M, Kriebel S, Salman KS, Greifenberg T, Hillemacher S, Rumpe B, Richenhagen J (2018) Improving model- based testing in automotive software engineering. In: 2018 IEEE/ACM 40th international conference on software engineering: software engineering in practice track (ICSE-SEIP), pp 172–180
19.
go back to reference Van Deursen A, Aniche M, Boone C, Cunha ML, Nadeem A (2019) Software quality and testing. Delft University of Technology Van Deursen A, Aniche M, Boone C, Cunha ML, Nadeem A (2019) Software quality and testing. Delft University of Technology
23.
go back to reference Maciel CP, Souza EF, Vijaykumar NL, Falbo RA, Meinerz GV, Felizardo KR (2018) An empirical study on the knowledge management practice in software testing. In: Experimental Software Engineering Latin American Workshop (ESELAW’18). XXI Ibero-American Conference on Software Engineering (CIBSE) Maciel CP, Souza EF, Vijaykumar NL, Falbo RA, Meinerz GV, Felizardo KR (2018) An empirical study on the knowledge management practice in software testing. In: Experimental Software Engineering Latin American Workshop (ESELAW’18). XXI Ibero-American Conference on Software Engineering (CIBSE)
25.
go back to reference Clegg BS, Rojas JM, Fraser G (2017) Teaching software testing concepts using a mutation testing game. In: 2017 IEEE/ACM 39th international conference on software engineering: software engineering education and training track (ICSE-SEET), pp 33–36. https://doi.org/10.1109/ICSE-SEET.2017.1 Clegg BS, Rojas JM, Fraser G (2017) Teaching software testing concepts using a mutation testing game. In: 2017 IEEE/ACM 39th international conference on software engineering: software engineering education and training track (ICSE-SEET), pp 33–36. https://​doi.​org/​10.​1109/​ICSE-SEET.​2017.​1
26.
go back to reference Angell R, Johnson B, Brun Y, Meliou A (2018) Themis: automatically testing software for discrimination. In: Proceedings of the 2018 26th ACM Joint Meeting on European software engineering conference and symposium on the foundations of software engineering, pp 871–875. https://doi.org/10.1145/3236024.3264590 Angell R, Johnson B, Brun Y, Meliou A (2018) Themis: automatically testing software for discrimination. In: Proceedings of the 2018 26th ACM Joint Meeting on European software engineering conference and symposium on the foundations of software engineering, pp 871–875. https://​doi.​org/​10.​1145/​3236024.​3264590
34.
go back to reference Shallue CJ, Vanderburg A (2018) Identifying exoplanets with deep learning: a five-planet resonant chain around kepler-80 and an eighth planet around kepler-90. Astron J 155(2):94CrossRef Shallue CJ, Vanderburg A (2018) Identifying exoplanets with deep learning: a five-planet resonant chain around kepler-80 and an eighth planet around kepler-90. Astron J 155(2):94CrossRef
35.
go back to reference Finlayson SG, Chung HW, Kohane IS, Beam AL (2018) Adversarial attacks against medical deep learning systems. arXiv preprint arXiv:1804.05296 Finlayson SG, Chung HW, Kohane IS, Beam AL (2018) Adversarial attacks against medical deep learning systems. arXiv preprint arXiv:1804.05296
36.
go back to reference Stadie BC, Yang G, Houthooft R, Chen X, Duan Y, Wu Y, Sutskever I (2018) Some considerations on learning to explore via meta-reinforcement learning. arXiv preprint arXiv:1803.01118. Stadie BC, Yang G, Houthooft R, Chen X, Duan Y, Wu Y, Sutskever I (2018) Some considerations on learning to explore via meta-reinforcement learning. arXiv preprint arXiv:1803.01118.
37.
go back to reference Raissi M, Perdikaris P, Karniadakis GE (2018) Multistep neural networks for data-driven discovery of nonlinear dynamical systems. arXiv preprint arXiv:1801.01236. Raissi M, Perdikaris P, Karniadakis GE (2018) Multistep neural networks for data-driven discovery of nonlinear dynamical systems. arXiv preprint arXiv:1801.01236.
41.
go back to reference Olson DL, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York Olson DL, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York
42.
go back to reference Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef
44.
go back to reference Grano G, Titov TV, Panichella, S., & Gall, H. C. (2018, March). How high will it be? using machine learning models to predict branch coverage in automated testing. In 2018 IEEE workshop on machine learning techniques for software quality evaluation (MaLTeSQuE), pp 19–24. https://doi.org/10.1109/MALTESQUE.2018.8368454 Grano G, Titov TV, Panichella, S., & Gall, H. C. (2018, March). How high will it be? using machine learning models to predict branch coverage in automated testing. In 2018 IEEE workshop on machine learning techniques for software quality evaluation (MaLTeSQuE), pp 19–24. https://​doi.​org/​10.​1109/​MALTESQUE.​2018.​8368454
46.
go back to reference Marcus G (2018) Deep learning: a critical appraisal. arXiv preprint arXiv:1801.00631 Marcus G (2018) Deep learning: a critical appraisal. arXiv preprint arXiv:1801.00631
47.
go back to reference Ma L, Juefei-Xu F, Zhang F, Sun J, Xue M, Li B, Zhao J (2018) Deepgauge: multi-granularity testing criteria for deep learning systems. In: Proceedings of the 33rd ACM/IEEE international conference on automated software engineering, pp. 120–131. https://doi.org/10.1145/3238147.3238202 Ma L, Juefei-Xu F, Zhang F, Sun J, Xue M, Li B, Zhao J (2018) Deepgauge: multi-granularity testing criteria for deep learning systems. In: Proceedings of the 33rd ACM/IEEE international conference on automated software engineering, pp. 120–131. https://​doi.​org/​10.​1145/​3238147.​3238202
48.
go back to reference Ma L, Zhang F, Xue M, Li B, Liu Y, Zhao J, Wang Y (2018) Combinatorial testing for deep learning systems. arXiv preprint arXiv:1806.07723 Ma L, Zhang F, Xue M, Li B, Liu Y, Zhao J, Wang Y (2018) Combinatorial testing for deep learning systems. arXiv preprint arXiv:1806.07723
49.
go back to reference Dwarakanath A, Ahuja M, Sikand S, Rao RM, Bose RJC, Dubash N, Podder S (2018) Identifying implementation bugs in machine learning based image classifiers using metamorphic testing. In: Proceedings of the 27th ACM SIGSOFT international symposium on software testing and analysis, pp 118–128. https://doi.org/10.1145/3213846.3213858 Dwarakanath A, Ahuja M, Sikand S, Rao RM, Bose RJC, Dubash N, Podder S (2018) Identifying implementation bugs in machine learning based image classifiers using metamorphic testing. In: Proceedings of the 27th ACM SIGSOFT international symposium on software testing and analysis, pp 118–128. https://​doi.​org/​10.​1145/​3213846.​3213858
52.
go back to reference Srisakaokul S, Wu Z, Astorga A, Alebiosu O, Xie T (2018) Multiple-implementation testing of supervised learning software. In: Workshops at the thirty-second AAAI conference on artificial intelligence Srisakaokul S, Wu Z, Astorga A, Alebiosu O, Xie T (2018) Multiple-implementation testing of supervised learning software. In: Workshops at the thirty-second AAAI conference on artificial intelligence
53.
go back to reference Sun Y, Huang X, Kroening D (2018) Testing deep neural networks. arXiv preprint arXiv:1803.04792 Sun Y, Huang X, Kroening D (2018) Testing deep neural networks. arXiv preprint arXiv:1803.04792
Metadata
Title
Deep Learning System and It’s Automatic Testing: An Approach
Author
Rijwan Khan
Publication date
05-11-2021
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 4/2023
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-021-00361-w

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