A Study on AI-based SW Similarity Detection Model Design (인공지능 기반 SW 유사성 탐지 모델 설계에 관한 연구)

Ahn cheolbum (안철범); KIM, JIN HONG (김진홍)

doi:10.29056/jsav.2023.3.05

A Study on AI-based SW Similarity Detection Model Design

Journal of Software Assessment and Valuation
Abbr : JSAV
2023, 19(1), pp.39-44
DOI : 10.29056/jsav.2023.3.05
Publisher : Korea Software Assessment and Valuation Society
Research Area : Engineering > Computer Science
Received : March 2, 2023
Accepted : March 20, 2023
Published : March 31, 2023

Ahn cheolbum ¹, KIM, JIN HONG ²

¹서일대학교
²배재대학교

Accredited

ABSTRACT

Currently, we live in an era called the Fourth Industrial Revolution, and as a key technology representing this era, we use artificial intelligence instead of computers for most of the intelligence activities that can be performed using the human brain. Various systems and SW are gradually evolving due to artificial intelligence, and SW usability is increasing very rapidly. However, as SW usability increases, problems such as reproduction and misuse occur, and research institutes and companies are demanding SW similarity. Accordingly, this study aims to design a similarity detection model using artificial intelligence for SW replication emotion. The similarity detection model design of this study is based on the Few-Shot Learning in a similarity detection model proceeds with meta-learning using DataSet with sufficient data, the data with less data contained in each class, and the recognition and learning of deep learning that rationally expresses the human brain.

KEYWORDS

Fourth Industrial Revolution, AI, SW usability, SW similarity, Detection Model design

Citation status

* References for papers published after 2023 are currently being built.

[journal] 이규대 / 2016 / A detail item guideline for IT device evaluation / 한국소프트웨어감정평가학회 논문지 / 한국소프트웨어감정평가학회 12(1) : 21~25

[journal] 이규대 / 2018 / A Similarity of device driver on Embedded system / 한국소프트웨어감정평가학회 논문지 / 한국소프트웨어감정평가학회 14(1) : 27~32

[journal] 김시열 / 2019 / A Study on Application of Summary Procedure in Case of Software Appraisal / 한국소프트웨어감정평가학회 논문지 / 한국소프트웨어감정평가학회 15(2) : 25~33

[web] Gu, J. / 2019 / Joint Pedestrian and Body Part Detection via Semantic Relationship Learning / https://www.mdpi.com/2076-3417/9/4/752

[journal] Gao, H. / 2019 / Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification / Appl. Sci 9 : 1239~

[confproc] Ronneberger, O. / 2015 / U-net: Convolutional networks for biomedical image segmentation / International Conference on Medical Image Computing and Computer-Assisted Intervention : 234~241

[journal] Bouindour, S. / 2019 / An On-Line and Adaptive Method for Detecting Abnormal Events in Videos Using Spatio-Temporal ConvNet / Appl. Sci 9 : 757~

[journal] Ren, S. / 2015 / Faster r-cnn: Towards real-time object detection with region proposal networks / Adv. Neural Inf. Process. Syst 28 : 91~99

[web] Radac, M.B. / 2019 / Data-Driven Model-Free Tracking Reinforcement Learning Control with VRFT-based Adaptive Actor-Critic / https://www.mdpi.com/2076-3417/9/9/1807

[journal] Wei, C. / 2019 / Obtaining Human Experience for Intelligent Dredger Control: A Reinforcement Learning Approach / Appl. Sci 9 : 1769~

[journal] Zheng, H.T. / 2019 / A Deep Temporal Neural Music Recommendation Model Utilizing Music and User Metadata / Appl. Sci 9 : 703~

[journal] Y. LeCun / 2015 / Deep learning / Nature 521(7553) : 436~444

[journal] M. I. Jordan / 2015 / Machine learning: Trends perspectives and prospects / Science 349(6245) : 255~260

[web] K. Nagpal / 2018 / Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer / https://www.nature.com/articles/s41746-019-0112-2

[journal] K. Arulkumaran / 2017 / Deep reinforcement learning: A brief survey / IEEE Signal Process. Mag 34(6) : 26~38

[confproc] M. Gheisari / 2017 / A survey on deep learning in big data / Proc. IEEE Int. Conf. Comput. Sci. Eng. (CSE) : 173~180

[journal] S. Pouyanfar / 2018 / A survey on deep learning : Algorithms techniques and applications / ACM Comput. Surv 51(5) : 92~

[journal] R. Vargas / 2017 / Deep learning: A review / Proc. Adv. Intell. Syst. Comput 1(11) : 1~11

[journal] Xue-Wen Chen / 2014 / Big Data Deep Learning: Challenges and Perspectives / IEEE Access / Institute of Electrical and Electronics Engineers (IEEE) 2 : 514~525

[confproc] Y. LeCun / 2010 / Convolutional networks and applications in vision / Proc. IEEE Int. Symp. Circuits Syst : 253~256

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Journal of Software Assessment and Valuation 2023 KCI Impact Factor : 0.35

A Study on AI-based SW Similarity Detection Model Design

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* References for papers published after 2023 are currently being built.

Journal of Software Assessment and Valuation 2023 KCI Impact Factor : 0.35

A Study on AI-based SW Similarity Detection Model Design

ABSTRACT

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* References for papers published after 2023 are currently being built.