2023 Vol.6 Feb.No1

• Title: Migration learning-based fault warning method for embedded software
• Name: Peng Bin
• Company: (Mingde College of Guizhou University,Guizhou, Guiyang 550025,China)
• Abstract:

  The current conventional embedded software fault warning methods achieve dynamic detection of software faults by constructing program contracts, which leads to poor warning effects due to the lack of processing of input data. In this regard, a migration learning-based embedded software fault warning method is proposed. The source domain model is adjusted by migration learning to quickly construct the target domain model, initialize the decoder weights by weight binding method, construct the input model by combining the software source code, the set of information extracted from the source code and the set of test cases, and optimize the warning strategy. In the experiments, the early warning performance of the proposed method is verified. The analysis of the experimental results shows that when the proposed method is used to warn the software, the warning time required is short and has a high warning efficiency.

  
  

• Keyword: migration learning; embedded software; fault warning; self-encoder.
• DOI: 10.12250/jpciams2023090313
• Citation form: Peng Bin.Migration learning-based fault warning method for embedded software [J]. Computer Informatization and Mechanical System,2023,Vol.6,pp.55-60

References

[1] Kasiviswanathan S , Ramalingam D , Priya P . Analyzing and validating Anthropological issues in the Embedded Software Project using Software Failure Mode and Effects Analysis and Paired 'T' Test[ J]. Solid State Technology, 2021, 63(2S 2020):5548-5565.

[2] Jeong E , Jeong D , Ha S . Dataflow Model-based Software Synthesis Framework for Parallel and Distributed Embedded Systems[J]. ACM Transactions on Design Automation of Electronic Systems, 2021, 26(5):1-38.

[3] Zhang Z , Ma M , Wang H , et al. A fault diagnosis method for photovoltaic module current mismatch based on numerical analysis and statistics [J]. Solar Energy, 2021, 225(4):221-236.

[4] Zhu D , Cheng X , Yang L , et al. Information Fusion Fault Diagnosis Method for Deep-Sea Human Occupied Vehicle Thruster Based on Deep Belief Network [J]. IEEE Transactions on Cybernetics, 2021, PP(99):1-14.

[5] Xu M , Wang Y . An Imbalanced Fault Diagnosis Method for Rolling Bearing Based on Semi-Supervised Conditional Generative Adversarial Network With Spectral Normalization[J]. IEEE Access, 2021, 9:27736-27747.

[6] Ark A , Tcri B , Ntsa C . My way is the highway: the role of plasticity in learning complex migration routes - ScienceDirect[J]. Animal Behaviour, 2021, 174:161-167.

[7] Khangahi F D , Kiani F . Social Mobilization and Migration Predictions by Machine Learning Methods: a study case on Lake Urmia[J]. International Journal of Innovative Technology and Exploring Engineering, 2021, 10(6):123-127.

[8] Li J , Lei H , Tsai S B . Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application[J]. Wireless Communications and Mobile Computing, 2021, 2021(7):1-11.

[9] Guilford T , Padget O , Perrins C M , et al. Early-life development of contrasting outbound and return migration routes in a long -lived seabird[J]. Ibis, 2022, 164(2):596-602.

[10] Jolivet D , Pereira S . The dynamic welfare habitus and its impact on Brazilian migration to Lisbon and Barcelona[J]. Geoforum, 2021, 120(3):58-66.

[11] Cui K , To S M . Rural-to-Urban Migration, Strain, and Bullying Perpetration: The Mediating Role of Negative Emotions, Attitude Toward Bullying, and Attachment to School:[J]. International Journal of Offender Therapy and Comparative Criminology, 2021, 65(1):24-50.

[12] Hussain E . Machine Learning of the Reverse Migration Models for Population Prediction: a Review[J]. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(5):1830-1838.

[13] Qin X , Luo X , Wu Z , et al. Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning [J]. IEEE Access, 2021, 9:29416-29428.

[14] Hassan M M , Mirza T . Migration to Online Teaching-Learning in School Education during COVID-19[J]. Journal on Today s Ideas-Tomorrow s Technologies, 2021, 8(2):73-84.

[15] Pan Z , Qang Z , Jiang Y , et al. A Deep Transfer Learning-Based Approach for Power Safety Harness Detection [J]. Computer Simulation,(7) 95-101.