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RETRACTED ARTICLE: A Novel Framework in Software Engineering for Deep Learning

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This article was retracted on 03 November 2023

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Abstract

There are increasingly deep learning approaches being used in software engineering domain. While, there are no agreement on how to adopt deep learning in this new domain. This work presents a methodological framework for deep learning in software engineering. Firstly, this work summarizes how to evaluate deep learning results in software engineering. Secondly, this paper suggest methodological principle that can be used in this new scenario. We construct a methodological framework as a guideline for deep learning research in software engineering.

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References

  1. Cico O, et al. Exploring the intersection between software industry and software engineering education—a systematic mapping of software engineering trends. J Syst Softw. 2021;172:110736.

    Article  Google Scholar 

  2. Campero S. Hiring and intra-occupational gender segregation in software engineering. Am Sociol Rev. 2021;86(1):60–92.

    Article  Google Scholar 

  3. Lo SK, et al. A systematic literature review on federated machine learning: from a software engineering perspective. ACM Comput Surv (CSUR). 2021;54(5):1–39.

    Article  Google Scholar 

  4. Giray G. A software engineering perspective on engineering machine learning systems: state of the art and challenges. J Syst Softw. 2021;180:111031.

    Article  Google Scholar 

  5. Bader J, et al. AI in software engineering at Facebook. IEEE Softw. 2021;38:52–61.

    Article  Google Scholar 

  6. De Angelis G, Lonetti F. About the assessment of grey literature in software engineering. Eval Assess Softw Eng. 2021. https://doi.org/10.1145/3463274.3463362.

    Article  Google Scholar 

  7. Lin T, Huang J, Gao J. Flame detection based on SIFT algorithm and one class classifier with undetermined environment. Comput Sci. 2015;42(6A):231–5.

    Google Scholar 

  8. García-Holgado A, et al. Improvement of learning outcomes in software engineering: active methodologies supported through the virtual campus. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje. 2021;16:143–53.

    Article  Google Scholar 

  9. Vayyavur R. Software engineering for technological ecosystems. In: Research anthology on recent trends, tools, and implications of computer programming. IGI Global, 2021. p 598–611

  10. Prechelt L. On implicit assumptions underlying software engineering research. Eval Assess Softw Eng. 2021. https://doi.org/10.1145/3463274.3463356.

    Article  Google Scholar 

  11. Lin T, Gao J. Graphic user interface testing based on petri net. Appl Res Comput. 2016;33(3):768–72.

    Google Scholar 

  12. Fu X, Ma Y, Lin T. A novel image matching algorithm based on graph theory. Comput Appl Softw. 2016;33(12):156–9.

    Google Scholar 

  13. Lin T. Deep learning for IoT. In: 39th IEEE international performance computing and communications conference, 2020

  14. Tao Lin J, Gao XFu, Lin Y. A container–destructor–explorer paradigm to code smells detection. J Chin Comput Syst. 2016;37(3):469–73.

    Google Scholar 

  15. Clarke P, O’Connor RV. The situational factors that affect the software development process: towards a comprehensive reference framework. Inf Softw Technol. 2012;54(5):433–47.

    Article  Google Scholar 

  16. Tao Lin J, Gao X, Fu YM, Lin Y. A novel direct small world network model. J Shanghai Normal Univ. 2016;45(5):566–72.

    MATH  Google Scholar 

  17. Lin T, Fu X, Chen F, Li L. A novel approach for code smells detection based on deep learning. In: EAI international conference on applied cryptography in computer and communications, 2021

  18. Fenton N, Pfleeger SL, Glass RL. Science and substance: a challenge to software engineers. IEEE Softw. 1994;11(4):86–95.

    Article  Google Scholar 

  19. Garousi V, Mäntylä MV. When and what to automate in software testing? A multi-vocal literature review. Inf Softw Technol. 2016;76:92–117.

    Article  Google Scholar 

  20. Lin T, Gao J, Fu X, Ma Y, Lin Y. Extraction approach for software bug report. Comput Sci. 2016;43(6):179–83.

    Google Scholar 

  21. Inui K, Abe S, Hara K, Morita H, Sao C, Eguchi M, Sumida A, Murakami K, Matsuyoshi S. Experience mining: building a large-scale database of personal experiences and opinions from web documents. In: Proceedings of the 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, vol. 01, pp. 314–321. IEEE Computer Society, 2008

  22. Ivarsson M, Gorschek T. A method for evaluating rigor and industrial relevance of technology evaluations. Empir Softw Eng. 2011;16(3):365–95. https://doi.org/10.1007/s10664-010-9146-4.

    Article  Google Scholar 

  23. Lin T. A data triage retrieval system for cyber security operations center. Pennsylvania State University thesis, 2018

  24. Kitchenham B, Pfleeger SL. Principles of survey research: part 5: populations and samples. ACM SIGSOFT Softw Eng Notes. 2002;27(5):17–20.

    Article  Google Scholar 

  25. Lin T, Zhong C, John Y, Liu P. Retrieval of relevant historical data triage operations in security operations center. In: From Database to cyber security. Lecture notes in computer science, 2018

  26. Kurashima T, Tezuka T, Tanaka K. Mining and visualizing local experiences from blog entries. In: DEXA, p 213–222. Springer, 2006

  27. Lin T, Gao J, Fu X, Lin Y. A novel bug report extraction approach. In: 15th International conference on algorithms and architectures for parallel processing, 2015, pp 771–780

  28. Lakshmanan G, Oberhofer M. Knowledge discovery in the blogosphere: approaches and challenges. IEEE Internet Comput. 2010;14(2):24–32.

    Article  Google Scholar 

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Authors and Affiliations

  1. Tao Algorithm, Sunnyvale, USA

    Tao Lin & Xue Fu

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  1. Tao Lin
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  2. Xue Fu
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Correspondence to Tao Lin.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s42979-023-02450-4

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Lin, T., Fu, X. RETRACTED ARTICLE: A Novel Framework in Software Engineering for Deep Learning. SN COMPUT. SCI. 3, 320 (2022). https://doi.org/10.1007/s42979-022-01173-2

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