A Review of Software Reliability Testing Techniques

被引：0
作者：
Jiang Z. [1 ]
Li H. [1 ]
Wang R. [1 ]
Zhang J. [1 ]
Li X. [1 ]
Zhang M. [1 ]
Wang P. [1 ]
机构：
[1] Jiang, Zhouxian
[2] Li, Honghui
[3] Wang, Rui
[4] Zhang, Junwen
[5] Li, Xiuru
[6] Zhang, Meng
[7] Wang, Penghao
来源：
Jiang, Zhouxian (zhouxianjiang@bjtu.edu.cn) | 1600年 / University of Zagreb, Faculty of Political Sciences卷 / 28期
关键词：
evaluation methods; intelligent software; software reliability; test framework; test methods;
D O I：
10.20532/cit.2020.1005155
中图分类号：
学科分类号：
摘要：
In the era of intelligent systems, the safety and reliability of software have received more attention. Software reliability testing is a significant method to ensure reliability, safety and quality of software. The intelligen software technology has not only offered new opportunities but also posed challenges to software reliability technology.The focus of this paper is to explore the software reliability testing technology under the impact of intelligent software technology. In this study, the basic theories of traditional software and intelligent software reliability testing were investigatedvia related previous works, and a general software reliability testing framework was established. Then, the technologies of software reliability testing were analyzed, including reliability modeling, test case generation, reliability evaluation, testing criteria and testing methods. Finally, the challenges and opportunities of software reliability testing technology were discussed at the end of this paper. © 2020, Journal of Computing and Information Technology. All Rights Reserved.
引用
收藏
页码：147 / 164
页数：17
相关论文
共 148 条
  • [1] Palviainen M., Et al., The Reliability Estimation, Prediction and Measuring of Component-Based Software, Journal of Systems and Software, 84, 6, pp. 1054-1070, (2011)
  • [2] Simonyan K., Zisserman A., ''Very Deep Con-volutional Networks for Large-Scale Image Rec-ognition'', (2014)
  • [3] Hinton G., Et al., Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, IEEE Signal Processing Magazine, 29, 6, pp. 82-97, (2012)
  • [4] Greenspan H., Et al., Guest Editorial Deep Learn-ing in Medical Imaging: Overview and Future Promise of an Exciting New Technique, IEEE Transactions on Medical Imaging, 35, 5, pp. 1153-1159, (2016)
  • [5] Vinyals O., Et al., Show and Tell: A Neural Image Caption Generator', Proc. of the 2015 IEEE Conference on Computer Vision and Pattern Rec-ognition (CVPR), pp. 3156-3164, (2015)
  • [6] Bojarski M., Et al., End to End Learning for Self-Driving Cars, (2016)
  • [7] Grosse K., Et al., Adversarial Examples for Mal-ware Detection', Proc. of the European Sym-posium on Research in Computer Security, (2017)
  • [8] Pei K., Et al., Deepxplore: Automated Whitebox Testing of Deep Learning Systems, Proc. of the 26th Symposium on Operating Systems Prin-ciples, pp. 1-18, (2017)
  • [9] Tian Y., Et al., DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars, Proc. of the International Conference on Software Engineering (ICSE), (2018)
  • [10] Zhang M., Et al., ''DeepRoad: GAN-Based Met-amorphic Autonomous Driving System Test-ing'', (2018)
12345678910