Defect Prediction of Radar System Software based on Bug Repositories and Behavior Models

doi:10.23940/ijpe.20.02.p11.284296

Abstract

Abstract:

Software plays an important role in radar products. Software quality has become one of the key factors of radar quality. The application of defect prediction may help understand the possible distribution of defects and therefore gain confidence regarding radar software quality. With a repository of software bugs and behavior models, a defect prediction approach based on the system-theoretic accident modeling process (STAMP) is proposed for radar system software. Firstly, a radar system software control model is built based on STAMP, the bug repository, and behavior models. A Bayesian network learning model is then constructed on process control models, and a training process is conducted on bug repositories to obtain defect prediction rules. Finally, the rules are applied on targeted radar software to predict possible defects. To verify the effectiveness and applicability of the proposed approach, a case study is also given on some typical radar system software.

Key words: software testing, radar system, defect prediction, system-theoretic accident modeling process, Bayesian network

Xi Liu, Zhiyong Zhao, Haifeng Li, Chang Liu, and Shengli Wang. Defect Prediction of Radar System Software based on Bug Repositories and Behavior Models [J]. Int J Performability Eng, 2020, 16(2): 284-296.

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References 18

1	1Q. Wang, S. J. Wu and M. S. Li, “Software Defect Prediction,” Journal of Software, vol. 19, no.7, pp. 1565-1580, July 2008.
2	2T. Hall, S. Beecham, D. Bowes, D. Gray and S. Counsell, “A Systematic Literature Review on Fault Prediction Performance in Software Engineering,” IEEE Transactions on Software Engineering, vol. 38, no.6, pp. 1276-1304, January 2012.
3	3X. Chen, Q. Gu, W. S. Liu, S. L. Liu and C. Ni, “Survey of Static Software Defect Prediction,” Journal of Software, vol. 27, no.1, pp. 1-25, November 2016.
4	4SAE International, ARP4761-Guidelines and Methods for Conducting the Safety Assessment Process on Civil Airborne Systems and Equipment, 1996.
5	5Department of Defense Standard Practice: System Safety, "Military Standard(MIL-STD) 882E", 2012.
6	6NASA Office of Safety and Mission Assurance, "NASA-STD- 8719. "NASA-STD- 8719.13B Software Safety Standard w/Change 1," 2004.
7	7Electronic Information Foundation of General Armaments Department, “The Guidelines for Designing Military Software Security:GJB/Z 102A,” 2012
8	8N. Leveson, “A new accident model for engineering safer systems,” Safety Science, vol. 42, no.4, pp. 237-270, April 2004.
9	9T. Ishimatsu, N. G. Leveson, J. P. Thomas, C. H. Fleming, M. Katahira, Y. Miyamoto, R. Nakao and N. Hoshino, “Hazard Analysis of Complex Spacecraft Using Systems-Theoretic Process Analysis,” Journal of Spacecraft and Rockets, vol. 51, no.2, pp. 509-522, February 2004.
10	10C. H. Fleming, M. Spencer, J. Thomas, N. Leveson and C. Wilkinson, “Safety assurance in NextGen and complex transportation systems,” Safety Science, vol. 55, no.2, pp. 173-187, June 2013.
11	11L. Zheng, and J. Hu, “Safety analysis of wheel brake system based on STAMP/STPA,” vol. 38, no.1, pp. 241-251, January 2017.
12	12H. W. Yan, “The research on the safety analysis of the railway transition based on STAMP,” Beijing Jiaotong University, Beijing China, June 2016
13	13R. Malhotra, “A systematic review of machine learning techniques for software fault prediction,” Applied Soft Computing Journal, vol. 27, pp. 504-518, February 2015.
14	14Y. Li, Z. Q. Huang, Y. Wang and B. W. Fang, “Survey on Data Driven Software Defects Prediction,” Acta Electronica Sinica, vol. 45, no.4, pp. 982-988, April 2017.
15	15A. Okutan, and O. T. Yildiz, “Software defect prediction using Bayesian networks,” Empirical Software Engineering, vol. 19, no.1, pp. 154-181, February 2014.
16	16I. H.Laradji, M. Alshayed and L. Ghouti, “Software defect prediction using ensemble learning on selected features,'' Information & Software Technology, vol. 58, pp. 388-402, 2015.
17	17Malhotra and Ruchika, “Comparative analysis of statistical and machine learning methods for predicting faulty modules,” Applied Soft Computing, vol. 21, no.21, pp. 286-297, August 2014.
18	18A. Kaur, and I. Kaur, “An empirical evaluation of classification algorithms for fault prediction in open source projects,” Journal of King Saud University - Computer and Information Sciences, vol. 30, no.1, pp. 2-17, January 2018

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