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software Defect Prediction Method Based on Rule Knowledge Extraction Model

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Journal of Donghua University(English Edition) 2018年第5期35卷 422-425页

作者：柴海燕闫然韩新宇唐龙利 china shipbuilding software quality&reliability testing center Beijing 100081China

The software defects are managed through the knowledge base,and defect management is upgraded from the data level to the knowledge level. The rule knowledge is mined from bug data based on a rule-based knowledge extraction model,and the appropriate strategy is configured in the strategy layer to predict software defects. The model is extracted by direct association rules and extended association rules,which improve the prediction rate of related defects and the efficiency of software testing.

关键词： knowledge base software testing defect prediction association rule

Fault Prediction and Diagnosis of Warship Equipment Field Programmable Gate Array software

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Journal of Donghua University(English Edition) 2018年第5期35卷 426-429页

作者：刘泊江闫然柴海燕韩新宇唐龙利 china shipbuilding software quality&reliability testing center Beijing 100081China

In order to solve the current high failure rate of warship equipment field programmable gate array( FPGA) software,fault detection is not timely enough and FPGA detection equipment is expensive and so on. After in-depth research,this paper proposes a warship equipment FPGA software based on Xilinx integrated development environment( ISE) and ModelSim *** simulation and timing simulation to verify the correctness of the logic design of the FPGA,this method is very convenient to view the signal waveform inside the FPGA program to help FPGA test engineers to achieve FPGA fault prediction and diagnosis. This test method has important engineering significance for the upgrading of warship equipment.

关键词： Field Programmable Gate Array(FPGA) fault prediction diagnosis

Incorporating S-shaped testing-effort functions into NHPP software reliability model with imperfect debugging

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Journal of Systems Engineering and Electronics 2015年第1期26卷 190-207页

作者： Qiuying Li Haifeng Li Minyan Lu School of reliability and Systems Engineering Beihang University Science & Technology on reliability & Environmental Engineering Laboratory quality Technique center China Aero-polytechnology Establishment

testing-effort（TE） and imperfect debugging（ID） in the reliability modeling process may further improve the fitting and prediction results of software reliability growth models（SRGMs）. For describing the S-shaped varying trend of TE increasing rate more accurately, first, two S-shaped testing-effort functions（TEFs）, i.e.,delayed S-shaped TEF（DS-TEF） and inflected S-shaped TEF（IS-TEF）, are proposed. Then these two TEFs are incorporated into various types（exponential-type, delayed S-shaped and inflected S-shaped） of non-homogeneous Poisson process（NHPP）SRGMs with two forms of ID respectively for obtaining a series of new NHPP SRGMs which consider S-shaped TEFs as well as ID. Finally these new SRGMs and several comparison NHPP SRGMs are applied into four real failure data-sets respectively for investigating the fitting and prediction power of these new *** experimental results show that：（i） the proposed IS-TEF is more suitable and flexible for describing the consumption of TE than the previous TEFs;（ii） incorporating TEFs into the inflected S-shaped NHPP SRGM may be more effective and appropriate compared with the exponential-type and the delayed S-shaped NHPP SRGMs;（iii） the inflected S-shaped NHPP SRGM considering both IS-TEF and ID yields the most accurate fitting and prediction results than the other comparison NHPP SRGMs.

关键词： testing-effort(TE) imperfect debugging(ID) software reliability growth models(SRGMs) S-shaped non-homogeneous Poisson process(NHPP)

Model learning:a survey of foundations,tools and applications

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Frontiers of Computer Science 2021年第5期15卷 71-92页

作者： Shahbaz ALI Hailong SUN Yongwang ZHAO Beijing Advanced Innovation center for Big Data and Brain Computing Beihang University Beijing 100191China SKLSDE School of Computer Science and EngineeringBeihang UniversityBeijing 100191China School of software Beihang UniversityBeijing 100191China School of Cyber Science and Technology College of Computer ScienceZhejiang UniversityHangzhou 310058China

software systems are present all around us and playing their vital roles in our daily *** correct functioning of these systems is of prime *** addition to classical testing techniques,formal techniques like model checking are used to reinforce the quality and reliability of software ***,obtaining of behavior model,which is essential for model-based techniques,of unknown software systems is a challenging *** mitigate this problem,an emerging black-box analysis technique,called Model Learning,can be *** complements existing model-based testing and verification approaches by providing behavior models of blackbox systems fully *** paper surveys the model learning technique,which recently has attracted much attention from researchers,especially from the domains of testing and ***,we review the background and foundations of model learning,which form the basis of subsequent ***,we present some well-known model learning tools and provide their merits and shortcomings in the form of a comparison ***,we describe the successful applications of model learning in multidisciplinary fields,current challenges along with possible future works,and concluding remarks.

关键词： model learning active automata learning automata learning libraries/tools inferring behavior models testing and formal verification