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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