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    題名: 生成式AI與軟體開發者的相遇:賦能效果探討
    The impact of generative AI on software development: empowering developers
    作者: 江仲偉
    Jiang, Zhong Wei
    貢獻者: 周致遠
    Chou, Chih-Yuan
    江仲偉
    Jiang, Zhong Wei
    關鍵詞: 人工智慧
    軟體開發工具
    軟體開發者
    軟體開發
    賦能理論
    生成式AI
    Artificial intelligence
    Development tools
    Developers
    Empowerment theory
    Generative AI
    Software development
    日期: 2024
    上傳時間: 2024-09-04 14:05:33 (UTC+8)
    摘要: 本研究旨在探討生成式人工智慧（AI）工具—特別是 GitHub Copilot 和
    ChatGPT—對於不同技能層級開發者的賦能（empowerment）影響。研究採用質性研究方法，以一開發者為主群體的網路論壇進行個案研究，採訪了來自三種技能層級的開發者們，深入分析其在使用生成式 AI 工具過程中的經驗、面臨的挑戰、以及在各個開發階段的決策過程。通過檢視心理賦能的個人內在、互動和行為層面，本研究深入分析了生成式 AI 工具如何通過其各種功能為開發者賦能。本研究可為生成式 AI 工具、開發者賦能、與軟體開發流程之間的動態關係提供寶貴的見解，研究結果預期能提供軟體產業參考以幫助相關開發流程之決策擬定，並能增進大眾對於AI如何驅動軟體工程之理解。
    This research explores the impact of generative artificial intelligence (AI) tools, with a specific focus on GitHub Copilot and ChatGPT, on the empowerment of developers at varying skill levels. Using a qualitative approach, including a case study within an online forum for developers, this study interviews developers with diverse skill levels for gaining insight on their experiences, challenges, and decision-making processes across different stages. By examining the intrapersonal, interactional, and behavioral dimensions of psychological empowerment, this study offers valuable insights into how generative AI tools shape developer empowerment through the tools’ various functions. The findings are expected to inform industry practices, guide tool development, and further our understanding of the evolving landscape of AI-driven software development.
    參考文獻: Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429.
    Afanasieva, T. V., Zagaichuk, I. A., & Zhelepov, A. S. (2019, April). Framework for assessing professional growth of software developers. In Proceedings of the 2019 5th International Conference on Computer and Technology Applications (pp. 46-50).
    Aggarwal, K. K. (2005). Software engineering. New Age International.
    Ahmad, A., Waseem, M., Liang, P., Fahmideh, M., Aktar, M. S., & Mikkonen, T. (2023). Towards human-bot collaborative software architecting with chatgpt. In Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering (pp. 279-285).
    Aleti, A. (2023). Software testing of generative ai systems: Challenges and opportunities. In 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) (pp. 4-14). IEEE.
    Amann, S., Proksch, S., Nadi, S., & Mezini, M. (2016). A study of visual studio usage in practice. In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER) (Vol. 1, pp. 124-134). IEEE.
    Anderson, J. R. (1993). Problem solving and learning. American Psychologist, 48(1), 35-44.
    Atatsi, E. A., Stoffers, J., & Kil, A. (2019). Factors affecting employee performance: a systematic literature review. Journal of Advances in Management Research, 16(3), 329-351.
    Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62.
    Bandura, A., & Wessels, S. (1997). Self-efficacy (pp. 4-6). Cambridge: Cambridge University Press.
    Barenkamp, M., Rebstadt, J., & Thomas, O. (2020). Applications of AI in classical software engineering. AI Perspectives, 2(1), 1.
    Beach, L. R. (1993). Broadening the definition of decision making: The role of prechoice screening of options. Psychological Science, 4(4), 215-220.
    Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS Quarterly, 11(3), 369-386.
    Brasil-Silva, R., & Siqueira, F. L. (2022). Metrics to quantify software developer experience: A systematic mapping. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (pp. 1562-1569).
    Broström, J., & Nilsson, M. (2018). Examining the differences in code reading practices employed by junior and senior developers.
    Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (No. w31161). National Bureau of Economic Research.
    Chen, B., Wu, Z., & Zhao, R. (2023). From fiction to fact: the growing role of generative AI in business and finance. Journal of Chinese Economic and Business Studies, 21(4), 471-496.
    Conger, J. A., & Kanungo, R. N. (1988). The empowerment process: Integrating theory and practice. Academy of Management Review, 13(3), 471-482.
    DiCicco‐Bloom, B., & Crabtree, B. F. (2006). The qualitative research interview. Medical Education, 40(4), 314-321.
    Ebert, C., & Louridas, P. (2023). Generative AI for software practitioners. IEEE Software, 40(4), 30-38.
    Epstein, Z., Hertzmann, A., Creativity, I. o. H., Akten, M., Farid, H., Fjeld, J., Frank, M. R., Groh, M., Herman, L., & Leach, N. (2023). Art and the science of generative AI. Science, 380(6650), 1110-1111.
    Fagerholm, F., & Münch, J. (2012). Developer experience: Concept and definition. In 2012 International Conference on Software and System Process (ICSSP) (pp. 73-77). IEEE.
    Feldt, R., de Oliveira Neto, F. G., & Torkar, R. (2018). Ways of applying artificial intelligence in software engineering. In Proceedings of the 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (pp. 35-41).
    Flor, N. V., & Hutchins, E. L. (1991). Analyzing distributed cognition in software teams: A case study of team programming during perfective software maintenance. In Empirical Studies of Programmers: Fourth workshop (Vol. 36, p. 64).
    Gilson, F., Morales-Trujillo, M., & Mathews, M. (2020, June). How junior developers deal with their technical debt?. In Proceedings of the 3rd International Conference on Technical Debt (pp. 51-61).
    Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15-31.
    Glaser, B.G. (1978) Theoretical sensitivity. Mill Valley, CA: Sociology Press.
    Glaser, B., (1992) Emergence vs forcing; Basics of grounded theory analysis. Sociology Press, Mill Valley, CA.
    Gobil, A. R. M., Shukor, Z., & Mohtar, I. A. (2009, August). Novice difficulties in selection structure. In 2009 International Conference on Electrical Engineering and Informatics (Vol. 2, pp. 351-356). IEEE.
    Goldkuhl, G. (2019). The generation of qualitative data in information systems research: the diversity of empirical research methods. Communications of the Association for Information Systems, 44, 572-599.
    Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023). ChatGPT is not all you need. A State of the Art Review of large Generative AI models. arXiv preprint arXiv:2301.04655.
    Greiler, M., Storey, M. A., & Noda, A. (2022). An actionable framework for understanding and improving developer experience. IEEE Transactions on Software Engineering, 49(4), 1411-1425.
    Guo, X. (2021, June). Towards automated software testing with generative adversarial networks. In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S) (pp. 21-22). IEEE.
    Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14.
    Hamburg, D. A., & Adams, J. E. (1967). A perspective on coping behavior: Seeking and utilizing information in major transitions. Archives of General Psychiatry, 17(3), 277-284.
    Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022). " I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. arXiv preprint arXiv:2212.05856.
    Hughes, R. T., Zhu, L., & Bednarz, T. (2021). Generative adversarial networks–enabled human–artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends. Frontiers in Artificial Intelligence, 4, 604234.
    Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the theory of planned behaviour. Technology in Society, 102521.
    Jalil, S., Rafi, S., LaToza, T. D., Moran, K., & Lam, W. (2023, April). Chatgpt and software testing education: Promises & perils. In 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) (pp. 4130-4137). IEEE.
    Jenkins, J. C. (1983). Resource mobilization theory and the study of social movements. Annual Review of Sociology, 9(1), 527-553.
    Joblin, M., Apel, S., Hunsen, C., & Mauerer, W. (2017, May). Classifying developers into core and peripheral: An empirical study on count and network metrics. In 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) (pp. 164-174). IEEE.
    Jovanovic, M., & Campbell, M. (2022). Generative artificial intelligence: Trends and prospects. Computer, 55(10), 107-112.
    Kark, R., Shamir, B., & Chen, G. (2003). The two faces of transformational leadership: Empowerment and dependency. Journal of Applied Psychology, 88(2), 246.
    Ke, W., & Zhang, P. (2011). Effects of empowerment on performance in open-source software projects. IEEE Transactions on Engineering Management, 58(2), 334-346.
    Kieffer, C. H. (1984). Citizen empowerment: A developmental perspective. Prevention in Human Services, 3(2-3), 9-36.
    Kunnen, E. S., & Bosma, H. A. (2003). Fischer's skill theory applied to identity development: A response to Kroger. Identity: An International Journal of Theory and Research, 3(3), 247-270.
    Latorre, R. (2013). Effects of developer experience on learning and applying unit test-driven development. IEEE Transactions on Software Engineering, 40(4), 381-395.
    Lazzeretti, L., Innocenti, N., Nannelli, M., & Oliva, S. (2023). The emergence of artificial intelligence in the regional sciences: A literature review. European Planning Studies, 31(7), 1304-1324.
    LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
    Legard, R., Keegan, J., & Ward, K. (2003). In-depth interviews. Qualitative Research Practice: A Guide for Social Science Students and Researchers, 6(1), 138-169.
    Li, X., Jiang, Y., Rodriguez-Andina, J. J., Luo, H., Yin, S., & Kaynak, O. (2021). When medical images meet generative adversarial network: Recent development and research opportunities. Discover Artificial Intelligence, 1, 1-20.
    Liang, J. T., Yang, C., & Myers, B. A. (2024, February). A large-scale survey on the usability of AI programming assistants: Successes and challenges. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering (pp. 1-13).
    Locke, E. A., Schweiger, D. M., & Latham, G. P. (1986). Participation in decision making: When should it be used?. Organizational Dynamics, 14(3), 65-79.
    Locke, K. (2000). Grounded theory in management research. 1-160. Sage, London.
    Lyons, P., & Bandura, R. P. (2017). Manager stimulation of employee self-regulated learning. Industrial and Commercial Training, 49(5), 205-212.
    Mayer, R. E. (1997). From novice to expert. In Handbook of Human-Computer Interaction (pp. 781-795). North-Holland.
    McCarthy, J. (1959). Programs with common sense. In Proceedings of the Teddington Conference on the Mechanization of Thought Processes, page 75--91.
    McLeod, L., MacDonell, S. G., & Doolin, B. (2011). Qualitative research on software development: A longitudinal case study methodology. Empirical Software Engineering, 16, 430-459.
    Meyerson, G., & Dewettinck, B. (2012). Effect of empowerment on employees performance. Advanced Research in Economic and Management sciences, 2(1), 40-46.
    Miguel, M. C., Ornelas, J. H., & Maroco, J. P. (2015). Defining psychological empowerment construct: Analysis of three empowerment scales. Journal of Community Psychology, 43(7), 900-919.
    Mikkonen, T. (2016). Flow, intrinsic motivation, and developer experience in software engineering. In Proceedings of the International Conference on Agile Software Development: Agile Processes in Software Engineering and Extreme Programming (XP 2016). Lecture Notes in Business Information Processing. Springer.
    Muşlu, K., Brun, Y., Holmes, R., Ernst, M. D., & Notkin, D. (2012). Speculative analysis of integrated development environment recommendations. ACM SIGPLAN Notices, 47(10), 669-682.
    Myers, M. D., & Avison, D. (Eds.). (2002). Qualitative research in information systems: a reader. Sage Publications.
    Myers, M. D. (2019). Qualitative research in business and management. Sage Publications.
    Nguyen, N., & Nadi, S. (2022, May). An empirical evaluation of GitHub copilot's code suggestions. In Proceedings of the 19th International Conference on Mining Software Repositories (pp. 1-5).
    Nilsson, I. (2023). An empirical analysis of GitHub Copilot [Unpublished bachelor’s thesis]. Uppsala University. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1775041&dswid=-503
    Page, N., & Czuba, C. E. (1999). Empowerment: What is it. Journal of Extension, 37(5), 1-5.
    Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub copilot. arXiv preprint arXiv:2302.06590.
    Perkins, D. D., & Zimmerman, M. A. (1995). Empowerment theory, research, and application. American Journal of Community Psychology, 23, 569-579.
    Pothukuchi, A. S., Kota, L. V., & Mallikarjunaradhya, V. (2023). Impact of Generative AI on the Software Development Lifecycle (SDLC). International Journal of Creative Research Thoughts, 11(8), b287-b291.
    Qadir, J. (2023). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In 2023 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-9). IEEE.
    Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. Preprint. 1–12.
    Rahman, M. S. (2020). The advantages and disadvantages of using qualitative and quantitative approaches and methods in language “testing and assessment” research: A literature review. Journal of Education and Learning, 6(1), 102-112.
    Ram, B., & Pratima Verma, P. V. (2023). Artificial intelligence AI-based chatbot study of ChatGPT, Google AI Bard and Baidu AI. World Journal of Advanced Engineering Technology and Sciences, 8(01), 258-261.
    Rappaport, J. (1987). Terms of empowerment/exemplars of prevention: Toward a theory for community psychology. American Journal of Community Psychology, 15(2), 121-148.
    Ritchie, J., Lewis, J., Nicholls, C. M., & Ormston, R. (2003). Qualitative research practice (Vol. 757). London: Sage.
    Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137-172.
    Schach, S. R. (1990). Software engineering. Aksen associates.
    Seaman, C. B. (1999). Qualitative methods in empirical studies of software engineering. IEEE Transactions on Software Engineering, 25(4), 557-572.
    Selamat, M. A., & Windasari, N. A. (2021). Chatbot for SMEs: Integrating customer and business owner perspectives. Technology in Society, 66, 101685.
    Sobania, D., Briesch, M., Hanna, C., & Petke, J. (2023). An analysis of the automatic bug fixing performance of chatgpt. arXiv preprint arXiv:2301.08653.
    Speer, P. W., & Peterson, N. A. (2000). Psychometric properties of an empowerment scale: Testing cognitive, emotional, and behavioral domains. Social Work Research, 24(2), 109-118.
    Spreitzer, G. M. (1995). Psychological empowerment in the workplace: Dimensions, measurement, and validation. Academy of Management Journal, 38(5), 1442-1465.
    Spreitzer, G. M., Kizilos, M. A., & Nason, S. W. (1997). A dimensional analysis of the relationship between psychological empowerment and effectiveness, satisfaction, and strain. Journal of Management, 23(5), 679-704.
    Storey, M. A., Zimmermann, T., Bird, C., Czerwonka, J., Murphy, B., & Kalliamvakou, E. (2019). Towards a theory of software developer job satisfaction and perceived productivity. IEEE Transactions on Software Engineering, 47(10), 2125-2142.
    Strauss, A., & Corbin, J. (1998). Basics of qualitative research techniques. Sage Publications.
    Stukas, A. A., & Dunlap, M. R. (2002). Community involvement: Theoretical approaches and educational initiatives. Journal of Social Issues, 58(3), 411-427.
    Sun, H., Nie, Y., Li, X., Huang, M., Tian, J., & Kong, W. (2022). An Automatic code generation method based on sequence generative adversarial network. 2022 7th IEEE International Conference on Data Science in Cyberspace (DSC).
    Sun, H., Teh, P., Ho, K. & Lin, B. (2017). Team diversity, learning, and innovation: a mediation model Journal of Computer Information Systems, 57(1), 22-30.
    Surameery, N. M. S., & Shakor, M. Y. (2023). Use chat gpt to solve programming bugs. International Journal of Information Technology & Computer Engineering (IJITC), 3(01), 17-22.
    Swift, C., & Levin, G. (1987). Empowerment: An emerging mental health technology. Journal of Primary Prevention, 8, 71-94.
    Takahashi, S., Chen, Y., & Tanaka-Ishii, K. (2019). Modeling financial time-series with generative adversarial networks. Physica A: Statistical Mechanics and its Applications, 527, 121261.
    Tarafdar, M., Beath, C. M., & Ross, J. W. (2019). Using AI to enhance business operations. MIT Sloan Management Review, 60(4), 37-44.
    Taulli, T. (2023). Auto code generation: How generative AI will revolutionize development. In Generative AI: How ChatGPT and Other AI Tools Will Revolutionize Business (pp. 127-143). Springer.
    Tenney, I., Das, D., & Pavlick, E. (2019). BERT rediscovers the classical NLP pipeline. arXiv preprint arXiv:1905.05950.
    Tessem, B. (2014). Individual empowerment of agile and non-agile software developers in small teams. Information and Software Technology, 56(8), 873-889.
    Thomas, K. W., & Velthouse, B. A. (1990). Cognitive elements of empowerment: An “interpretive” model of intrinsic task motivation. Academy of Management Review, 15(4), 666-681.
    Vaithilingam, P., Zhang, T., & Glassman, E. L. (2022, April). Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Proceedings of the CHI Conference on Human Factors in Computing Systems Extended Abstracts (pp. 1-7).
    Viana, D., Conte, T., & de Souza, C. R. (2014). Knowledge transfer between senior and novice software engineers: A qualitative analysis. In Proceedings of the 26th International Conference on Software Engineering & Knowledge Engineering (SEKE 2014) (pp. 235-240).
    Wall, T. D., Cordery, J. L., & Clegg, C. W. (2002). Empowerment, performance, and operational uncertainty: A theoretical integration. Applied Psychology, 51(1), 146-169.
    Wallston, K. A., Wallston, B. S., Smith, S., & Dobbins, C. J. (1987). Perceived control and health. Current Psychology, 6, 5-25.
    Walters, W. P., & Murcko, M. (2020). Assessing the impact of generative AI on medicinal chemistry. Nature Biotechnology, 38(2), 143-145.
    White, J., Hays, S., Fu, Q., Spencer-Smith, J., & Schmidt, D. C. (2023). Chatgpt prompt patterns for improving code quality, refactoring, requirements elicitation, and software design. arXiv preprint arXiv:2303.07839.
    Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
    Yan, H., Zhang, H., Liu, L., Zhou, D., Xu, X., Zhang, Z., & Yan, S. (2022). Toward intelligent design: An ai-based fashion designer using generative adversarial networks aided by sketch and rendering generators. IEEE Transactions on Multimedia, 25, 2323-2338.
    Yang, F., Qian, J., Tang, L. and Zhang, L. (2016) No longer take a tree for the forest: a cross-level learning-related perspective on individual innovative behavior. Journal of Management & Organization, 22(3), 291-310.
    Zhang, C., Zhang, C., Zheng, S., Qiao, Y., Li, C., Zhang, M., Dam, S. K., Thwal, C. M., Tun, Y. L., & Huy, L. L. (2023). A complete survey on generative ai (aigc): Is chatgpt from gpt-4 to gpt-5 all you need? arXiv preprint arXiv:2303.11717.
    Zhou, M., & Mockus, A. (2010, November). Developer fluency: Achieving true mastery in software projects. In Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering (pp. 137-146).
    Ziegler, A., Kalliamvakou, E., Li, X. A., Rice, A., Rifkin, D., Simister, S., Sittampalam, G. & Aftandilian, E. (2022, June). Productivity assessment of neural code completion. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming (pp. 21-29).
    Zimmerman, M. A. (1990). Taking aim on empowerment research: On the distinction between individual and psychological conceptions. American Journal of Community Psychology, 18(1), 169-177.
    Zimmerman, M. A., Israel, B. A., Schulz, A., & Checkoway, B. (1992). Further explorations in empowerment theory: An empirical analysis of psychological empowerment. American Journal of Community Psychology, 20, 707-727.
    Zimmerman, M. A. (1995). Psychological empowerment: Issues and illustrations. American Journal of Community Psychology, 23, 581-599.
    Zimmerman, M. A. (2000). Empowerment theory: Psychological, organizational and community levels of analysis. In Handbook of Community Psychology (pp. 43-63). Boston, MA: Springer US.
    描述: 碩士
    國立政治大學
    資訊管理學系
    111356034
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0111356034
    資料類型: thesis
    顯示於類別:[資訊管理學系] 學位論文

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