문제 기술서에서 NLP와 온톨로지를 이용한 요구사항 구조화 단국대학교 대학원 컴퓨터과학과 소프트웨어학전공 백 영 윤 지도교수: 박 용 범 요구 분석은 소프트웨어 개발에 있어서 핵심 ...

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
https://www.riss.kr/link?id=T16943725
용인 : 단국대학교 대학원, 2024
학위논문(박사) -- 단국대학교 대학원 , 컴퓨터학과 소프트웨어학전공 , 2024. 2
2024
한국어
005.1 판사항(23)
경기도
Structuring Requirements Using NLP and Ontology in Problem Description Documents
v, 85p ; 30cm.
단국대학교 논문은 저작권에 의해 보호받습니다.
지도교수: 박용범
참고문헌 :78-83p
I804:11017-000000199679
0
상세조회0
다운로드문제 기술서에서 NLP와 온톨로지를 이용한 요구사항 구조화 단국대학교 대학원 컴퓨터과학과 소프트웨어학전공 백 영 윤 지도교수: 박 용 범 요구 분석은 소프트웨어 개발에 있어서 핵심 ...
문제 기술서에서 NLP와 온톨로지를 이용한 요구사항 구조화 단국대학교 대학원 컴퓨터과학과 소프트웨어학전공 백 영 윤 지도교수: 박 용 범 요구 분석은 소프트웨어 개발에 있어서 핵심 활동이며 소프트웨어 개발 주기에 전체에 큰 영향을 미친다. 따라서 요구 분석은 소프트웨어 개발에서 사용자가 원하는 소프트웨어를 만드는 데 있어서 중요한 과정이다. 요구 분 석이 중요하지만, 요구사항 분석은 여전히 어렵고 복잡하며 분석하는 사람마 다 각각의 분석 내용을 만들어 낸다. 또한 자연어 요구사항 분석은 자동화되 지 않고 시간과 노력이 많이 사용되며 오류 발생의 문제가 있다. 따라서 부 족한 요구사항 분석과 자동화되지 않은 요구사항 분석 시스템은 소프트웨어 개발에서 많은 문제를 만들어 낸다. 이러한 문제를 해결하기 위해서 본 논문 에서는 요구사항 문제 기술서에서 ChatGPT 기반의 요구사항 구조화 분석 프로그램을 만들고 자연어 처리와 거시구조 개념을 적용하여서 요구사항 분 석을 가능하게 하고 이를 온톨로지로 구조화하여서 유즈케이스 다이어그램 과 유즈케이스 스펙을 만들었다. 이를 통해 자연어 요구사항 문서 분석을 가 능하게 하고 자동화된 시스템을 제공하여서 요구사항 분석 결과를 분석할 수 있는 구조화 방법을 제안한다. 주제어: 요구 분석, 요구사항, ChatGPT, 온톨로지
목차 (Table of Contents)
참고문헌 (Reference)
1. Social engineering with ChatGPT, Grbic, D. V., Dujlovic, I., In 2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-5). IEEE, , 2023
2. ChatGPT: Its Applications and Limitations, Chowdhury, M. N. U. R., Haque, A., In 2023 3rd International Conference on Intelligent Technologies (CONIT) (pp. 1-7). IEEE, , 2023
3. Effectiveness of ChatGPT in Essay Autograding, Altamimi, A. B., In 2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE) (pp. 102-106). IEEE, , 2023
4. Towards queryable and traceable domain models, Guo, J. L., Mussbacher, G., Kienzle, J., Saini, R., In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 334-339). IEEE, , 2020
5. OpenAI ChatGPT as a Logical Interpreter of code, Bashir, S., Firdous, F., Rufai, S. Z., Kumar, S., In 2023 2nd International Conference on Edge Computing and Applications (ICECAA) (pp. 1192-1197). IEEE, , 2023
6. ChatGPT: Opportunities, Features and Future Prospects, Anusuya, V., Gowthami, D., Santhosh, R., Abinaya, M., In 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1614-1622). IEEE, , 2023
7. Rule-based extraction of goal-use case models from text, Nguyen, T. H., Grundy, J., Almorsy, M., In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (pp. 591-601, , 2015
8. Norbert Transfer learning for requirements classification, Koziolek, A., Hey, T., Keim, J., Tichy, W. F., In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 169-179). IEEE, , 2020
9. Structured Medical Dataset Analysis Tool Based on ChatGPT, Shin, Y. G., Park, J., Nam, J., Park, S., Choi, J., In 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 837-842). IEEE, , 2023
10. Automated goal model extraction from user stories using NLP, Güneş, T., Aydemir, F. B., In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 382-387). IEEE, , 2020
1. Social engineering with ChatGPT, Grbic, D. V., Dujlovic, I., In 2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-5). IEEE, , 2023
2. ChatGPT: Its Applications and Limitations, Chowdhury, M. N. U. R., Haque, A., In 2023 3rd International Conference on Intelligent Technologies (CONIT) (pp. 1-7). IEEE, , 2023
3. Effectiveness of ChatGPT in Essay Autograding, Altamimi, A. B., In 2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE) (pp. 102-106). IEEE, , 2023
4. Towards queryable and traceable domain models, Guo, J. L., Mussbacher, G., Kienzle, J., Saini, R., In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 334-339). IEEE, , 2020
5. OpenAI ChatGPT as a Logical Interpreter of code, Bashir, S., Firdous, F., Rufai, S. Z., Kumar, S., In 2023 2nd International Conference on Edge Computing and Applications (ICECAA) (pp. 1192-1197). IEEE, , 2023
6. ChatGPT: Opportunities, Features and Future Prospects, Anusuya, V., Gowthami, D., Santhosh, R., Abinaya, M., In 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1614-1622). IEEE, , 2023
7. Rule-based extraction of goal-use case models from text, Nguyen, T. H., Grundy, J., Almorsy, M., In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (pp. 591-601, , 2015
8. Norbert Transfer learning for requirements classification, Koziolek, A., Hey, T., Keim, J., Tichy, W. F., In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 169-179). IEEE, , 2020
9. Structured Medical Dataset Analysis Tool Based on ChatGPT, Shin, Y. G., Park, J., Nam, J., Park, S., Choi, J., In 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 837-842). IEEE, , 2023
10. Automated goal model extraction from user stories using NLP, Güneş, T., Aydemir, F. B., In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 382-387). IEEE, , 2020
11. Survey of works that transform requirements into UML diagrams, Abdouli, M., Ghezala, H. B., Karaa, W. B. A., In 2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) (pp. 117-123). IEEE, , 2016
12. Generating sequence diagram from natural language requirements, Jahan, M., Abad, Z. S. H., Far, B., In 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) (pp. 39-48). IEEE, , 2021
13. Evaluating ChatGPT for Smart Contracts Vulnerability Correction, Gatteschi, V., Napoli, E. A., In 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 1828-1833). IEEE, , 2023
14. Transformation of sysml requirement diagram into owl ontologies, Ashari, A., Sari, A. K., Wardhana, H., International Journal of Advanced Computer Science and Applications, 11(4), , 2020
15. Automatic terminology extraction and ranking for feature modeling, Hua, J., Liu, C., Zhang, J., Niu, N., Chen, S., In 2022 IEEE 30th International Requirements Engineering Conference (RE) (pp. 51-63). IEEE, , 2022
16. On Codex Prompt Engineering for OCL Generation: An Empirical Study, Hamdaqa, M., Abukhalaf, S., Khomh, F., arXiv preprint arXiv:2303.16244, , 2023
17. Towards an automatic requirements classification in a new Spanish dataset, Condori-Fernandez, N., Limaylla-Lunarejo, M. I., Luaces, M. R., In 2022 IEEE 30th International Requirements Engineering Conference (RE) (pp. 270-271). IEEE, , 2022
18. SVM machine learning classifier to automate the extraction of SRS elements, Imam, A. T., Alhroob, A., Alzyadat, W., International Journal of Advanced Computer Science and Applications (IJACSA), , 2021
19. Ambiguous software requirement specification detection: An automated approach, Osman, M. H., Zaharin, M. F., In Proceedings of the 5th International Workshop on Requirements Engineering and Testing (pp. 33-40, , 2018
20. Identifying use case elements from textual specification: A preliminary study, Tiwari, S., Mirani, Y., Sagar, S., Rathore, S. S., In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 410-411). IEEE, , 2020
21. Artificial Intelligence in Software Requirements Engineering: State-of-the-Art, Reddivari, K., Reddivari, S., Liu, K., In 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI) (pp. 106-111). IEEE, , 2022
22. 거시구조 개념을 이용한 요구사항 분석과 Use-case 도출 방법, 김선애, 조용균, 정보처리학회논문지 D, 18(6), 433-442, , 2011
23. Machine Learning-Based Run-Time DevSecOps: ChatGPT Against Traditional Approach, Petrović, N., preprint, 1-5, , 2023
24. Selection of requirement elicitation techniques: a neural network based approach, Farooqui, M. F., Alam, A., Sultan, A., Nazeer, J., Muqeem, M., 13(1), , 2022
25. ChatGPT: Information Retrieval from Image using Robotic Process Automation and OCR, Shubeeksh, K., Thakshith, V., Sanjana, R., Raman, V. M., Shidaganti, G., In 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1264-1270). IEEE, , 2023
26. Extracting Requirements Models from Natural-Language Document for Embedded Systems, Wang, C., Hou, L., Chen, X., In 2022 IEEE 30th International Requirements Engineering Conference Workshops (REW) (pp. 18-21). IEEE, , 2022
27. Knowledge-based sense disambiguation of multiword expressions in requirements documents, Tichy, W. F., Hey, T., Keim, J., In 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) (pp. 70-76). IEEE, , 2021
28. Towards expert systems for improved customer services using ChatGPT as an inference engine, Ezenkwu, C. P., Institute of Electrical and Electronics Engineers, , 2023
29. A collaborative approach for effective requirement elicitation in oblivious client environment, Ayub, N., Haq, N. U., Sarwar, M. U., Hanif, M. K., Talib, M. R., Mansoor, A., 8(6), , 2017
30. A natural language processing technique for formalization of systems requirement specifications, Gerstner, E., Gambardella, C., Mirakhorli, M., Cassetti, J., Zappavigna, M., Koscinski, V., In 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) (pp. 350-356). IEEE, , 2021
31. Interactive and visual prompt engineering for ad-hoc task adaptation with large language models, Pfister, H., Rush, A. M., Hoover, B., Webson, A., Sanh, V., Beyer, J., Strobelt, H., IEEE transactions on visualization and computer graphics, 29(1), 1146-1156, , 2022
32. Requirements dependency extraction by integrating active learning with ontology-based retrieval, Deshpande, G., Biesialska, K., Palomares, C., Ho, J., Kamra, I., Motger, Q., Franch, X., In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 78-89). IEEE., , 2020
33. Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software Traceability, Dearstyne, K. R., Rodriguez, A. D., Cleland-Huang, J., In 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW) (pp. 455-464). IEEE, , 2023
34. Harnessing the Power of ChatGPT to Decimate Mis/Disinformation: Using ChatGPT for Fake News Detection, Caramancion, K. M., In 2023 IEEE World AI IoT Congress (AIIoT) (pp. 0042-0046). IEEE, , 2023
35. ChatGPT and Generative AI Guidelines for Addressing Academic Integrity and Augmenting Pre-Existing Chatbots, El-Ayoubi, M., Alahakoon, D., De Silva, D., Mills, N., Manic, M., In 2023 IEEE International Conference on Industrial Technology (ICIT) (pp. 1-6). IEEE, , 2023
36. Chat2vis: Generating data visualisations via natural language using chatgpt, codex and gpt-3 large language models, Maddigan, P., Susnjak, T., IEEE Access, , 2023
37. Emerging Requirement Engineering Models: Identifying Challenges is Important and Providing Solutions is Even Better, Yousaf, A., Moqeet, A. A., Ali, H. W., Noor, H., Tariq, M., Naseer, O., Hamid, A. B., 12(11)., , 2021
38. Using ChatGPT standard prompt engineering techniques in lesson preparation: role, instructions and seed-word prompts, Janković, D. S., Spasić, A. J., In 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) (pp. 47-50). IEEE, , 2023