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Open-source Software Reliability Modeling with Stochastic Impulsive Differential Equations
Yao Hu 1
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School of Mathematics and Statistics, Guizhou University, China
School of Data Science, Guizhou Institute of Technology, China
Submission date: 2023-02-02
Final revision date: 2023-02-23
Acceptance date: 2023-05-20
Online publication date: 2023-05-21
Corresponding author
Jianfeng Yang   

School of Data Science, Guizhou Institute of Technology, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(2):166342
  • Reliability modeling with stochastic impulsive differential equations (SIDE) is proposed.
  • The model divides dynamic process of software fault into a continuous and a skipped part.
  • The proposed model with SIDE is more in line with reality and has a better fitting effect.
In reality, sudden updates of software, attacks of hackers, influence of the Internet market, etc. can cause a surge in the number of open-source software (OSS) faults (this moment is the time when impulse occurs), which results in impulsive phenomenon. For the existing software reliability models, dynamic process of software fault is considered to be continuous when assessing reliability, but continuity of the process can be disrupted with appearance of random impulses. Thus, to more accurately assess software reliability, we proposed an OSS reliability model with SIDE. In the model, dynamic process of software fault is divided into a continuous and a skipped part, described the continuous part of the process with SDE, and described destruction of the continuity caused by unpredictable random events with random impulses. Finally, the proposed model is verified with two datasets from real OSS project, and the results show that the proposed model is more in line with reality and has better fitting effect than the existing models.
This work was supported by National Natural Science Foundation of China (No.71901078, 72161005), Science and Technology Foundation of Guizhou (No. QianKeHeJiChu-ZK[2022]-General184), Guizhou Internet + Industrial Technology Research Institute and Key Laboratory of Electric Power Big Data of Guizhou Province.
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