Analyst’s Perception on the Use of AI-based Tools in the Software Development Life Cycle

Keywords: artificial intelligence, sdlc, integration, challenge, key factor

Abstract

Artificial Intelligence (AI) integration has been the goal in many industries, including in the software development industry. One example of this integration comes in the form of integrating AI in the Software Development Lifecycle (SDLC). To date, the difficulties of incorporating AI-based tools into particular phases of SDLC have not received much attention in research. Using qualitative approach, this study aims to discover the perception on the use of AI-based tools and challenges in integrating them in the analysis phase of SDLC. The study finds out that analyst have positive perception about integrating this technology in their field of work but there are some challenges while integrating this technology such as data security and reliability concern, dependency, and adapting to this technology. This study also discovers some key factors of why some analysts adopt or refuse this technology namely time, urgency, budget, and insecurities of the users.

Downloads

Download data is not yet available.

Author Biographies

Rafi Giffari, University of Indonesia

Rafi Giffari is a Master Student in the Faculty of Computer Science at the University of Indonesia. He majored in Information Systems and graduated from Binus University in 2022. He has an interest in the field of Information Systems, including user interface, user experience, and software development. For further information, he can be reached at rafi.giffari@ui.ac.id and https://orcid.org/0009-0008-4422-2015

M. Mushlih Ridho, University of Indonesia

M. Mushlih Ridho is a Master Student in the Faculty of Computer Science at the University of Indonesia. His Previous study is a Bachelor degree of Information Systems at Faculty of Science and Technology at the Jambi University in 2023. He has an interest in the field of E-Health and telemedicine. He can be contacted at email: m.mushlih@ui.ac.id and https://orcid.org/0009-0009-4512-0632

Dana Indra Sensuse, University of Indonesia

Dana Indra Sensuse is a Professor, Lecturer, and Researcher at the University of Indonesia's Faculty of Computer Science in Depok, Indonesia. He specializes in knowledge management (KM), e-government, e-business, information systems (IS)/IT, management and governance, and data science and analytics. In 2004, he received his PhD from the University of Toronto. He has over 300 globally indexed scholarly articles and over 1,400 citations. For further inquiries or collaboration, Dana Indra Sensuse, the corresponding author, can be reached at dana@cs.ui.ac.id and https://orcid.org/0000-0002-0012-8552

Deden Sumirat Hidayat, National Research and Innovation Agency

Deden Sumirat Hidayat is a researcher at the National Research and Innovation Agency/ Badan Riset dan Inovasi Nasional (BRIN). He graduated as a doctor of computer science from the Faculty of Computer Science, University of Indonesia, Depok, Indonesia in July 2023. He obtained his master degree of Computer Sciences at Bogor Agricultural University in 2016. He has an interest in the fields of KM, Information System, Management & Governance, data science, information and library science. He has experience in developing data mining-based knowledge management systems, particularly such as decision support systems. He also has experience as a web-based information system consultant and developer. He can be contacted at email: deden.sumirat@ui.ac.id and dede025@brin.go.id and https://orcid.org/0000-0002-1847-8665

Erisva Hakiki Purwaningsih, University of Indonesia

Erisva Hakiki Purwaningsih is a doctoral student in the Faculty of Computer Science at the University of Indonesia, located in Depok, Indonesia. Her academic journey includes a master’s degree in Computer Sciences, which she successfully earned from Putra Indonesia University in 2008. Alongside her academic pursuits, she actively contributes to research as a valued member of the Ministry of Informatics and Communication, Republic of Indonesia. Erisva's professional interests span across various domains, including Supply Chain Management, Information Systems, Management, and Governance. As a fervent advocate for progress and innovation, Erisva Hakiki Purwaningsih has demonstrated her commitment to advancing knowledge and solutions within her areas of expertise. erisvaha.kiki@ui.ac.id and https://orcid.org/0009-0009-3156-9316

References

Acharya, B., and Sahu, P. K. 2020. “Software Development Life Cycle Models: A Review Paper,” International Journal of Advanced Research in Engineering and Technology (IJARET) (11:12), pp. 169–176.
Adanna, A. A., and Nonyelum, O. F. 2020. “Criteria for Choosing the Right Software Development Life Cycle Method for the Success of Software Project,” IUP Journal of Information Technology (16:2), pp. 39–65.
Al-Saqqa, S., Sawalha, S., and AbdelNabi, H. 2020. “Agile Software Development: Methodologies and Trends,” International Journal of Interactive Mobile Technologies (14:11), pp. 246–270.
Banerjee, P., Kumar, B., Singh, Amarnath, Singh, Arundhati, and Kumari, R. 2020. “Efficiency Analysis of Software Development Life Cycle Models,” International Journal of Computer Science Trends and Technology (IJCST) (8:2), pp. 152–162.
Ben-Zahia, M. A., and Jaluta, I. 2014. “Criteria for Selecting Software Development Models,” in 2014 Global Summit on Computer & Information Technology (GSCIT), IEEE, pp. 1–6.
Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., and De Felice, F. 2020. “Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions,” Sustainability (12:2), p. 492.
Creswell, J. W., and Creswell, J. D. 2018. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, (5th ed.), SAGE Publications, Inc.
Drogt, J., Milota, M., Vos, S., Bredenoord, A., and Jongsma, K. 2022. “Integrating Artificial Intelligence in Pathology: A Qualitative Interview Study of Users’ Experiences and Expectations,” Modern Pathology (35:11), pp. 1540–1550.
Enholm, I. M., Papagiannidis, E., Mikalef, P., and Krogstie, J. 2022. “Artificial Intelligence and Business Value: A Literature Review,” Information Systems Frontiers (24), Information Systems Frontiers, pp. 1709–1734.
Goralski, M. A., and Tan, T. K. 2020. “Artificial Intelligence and Sustainable Development,” The International Journal of Management Education (18:1), p. 100330.
Gupta, A., Rawal, A., and Barge, Y. 2021. “Comparative Study of Different SDLC Models,” International Journal for Research in Applied Science & Engineering Technology (IJRASET) (9:11), pp. 73–80.
Jerhamre, E., Carlberg, C. J. C., and van Zoest, V. 2022. “Exploring the Susceptibility of Smart Farming: Identified Opportunities and Challenges,” Smart Agricultural Technology (2), p. 100026.
Kiger, M. E., and Varpio, L. 2020. “Thematic Analysis of Qualitative Data: AMEE Guide No. 131,” Medical Teacher (42:8), Taylor & Francis, pp. 846–854.
KUANG, L., LIU, H., REN, Y., LUO, K., SHI, M., SU, J., and LI, X. 2021. “Application and Development Trend of Artificial Intelligence in Petroleum Exploration and Development,” Petroleum Exploration and Development (48:1), pp. 1–14.
Laato, S., Mäntymäki, M., Minkkinen, M., Birkstedt, T., Islam, A. K. M. N., and Dennehy, D. 2022. “Integrating Machine Learning With Software Development Lifecycles: Insights From Experts,” in ECIS 2022 Research Papers, pp. 1–16.
Mohammed, T. A., Qasim, M. N., and Bayat, O. 2021. “Hybrid Solution of Challenges Future Problems in the New Generation of the Artificial Intelligence Industry Used Operations Research Industrial Processes,” in International Conference on Data Science, E-Learning and Information Systems 2021, pp. 213–218.
Moreschini, S., Hästbacka, D., and Taibi, D. 2023. “MLOps Pipeline Development: The OSSARA Use Case,” in Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems, RACS ’23, New York, NY, USA: Association for Computing Machinery, pp. 1–8.
Nortje, M. A., and Grobbelaar, S. S. 2020. “A Framework for the Implementation of Artificial Intelligence in Business Enterprises: A Readiness Model,” in 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1–10.
Okesola, O. J., Adebiyi, A. A., Owoade, A. A., Adeaga, O., Adeyemi, O., and Odun-Ayo, I. 2020. “Software Requirement in Iterative SDLC Model,” in Intelligent Algorithms in Software Engineering: Proceedings of the 9th Computer Science On-Line Conference 2020, pp. 26–34.
Panda, G., Upadhyay, A. K., and Khandelwal, K. 2019. “Artificial Intelligence: A Strategic Disruption in Public Relations,” Journal of Creative Communications (14:3), pp. 196–213.
Pargaonkar, S. 2023. “A Comprehensive Research Analysis of Software Development Life Cycle (SDLC) Agile & Waterfall Model Advantages, Disadvantages, and Application Suitability in Software Quality Engineering,” International Journal of Scientific and Research Publications (13:8), pp. 120–124.
Saravanan, T., Jha, S., Sabharwal, G., and Narayan, S. 2020. “Comparative Analysis of Software Life Cycle Models,” in 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), pp. 906–909.
Sarker, I. H. 2022. “AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems,” SN Computer Science (3:158).
Stavridis, A., and Drugge, A. 2023. “The Rise of Intelligent System Development: A Qualitative Study of Developers’ Views on AI in Software Development Processes,” Umeå universitet.
Vorobeva, D., El Fassi, Y., Costa Pinto, D., Hildebrand, D., Herter, M. M., and Mattila, A. S. 2022. “Thinking Skills Don’t Protect Service Workers from Replacement by Artificial Intelligence,” Journal of Service Research (25:4), pp. 601–613.
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., and Trichina, EleniArtificial intelligence, robotics, advanced technologies and human resource management: a systematic review. 2022. “Artificial Intelligence, Robotics, Advanced Technologies and Human Resource Management: A Systematic Review,” The International Journal of Human Resource Management (33:6), pp. 1237–1266.
Waseem, M., Das, T., Ahmad, A., Fehmideh, M., Liang, P., and Mikkonen, T. 2023. Using ChatGPT throughout the Software Development Life Cycle by Novice Developers. (http://arxiv.org/abs/2310.13648).
Xu, Y., Liu, Xin, Cao, X., Huang, C., Liu, E., Qian, S., Liu, Xingchen, Wu, Y., Dong, F., Qiu, C. W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., Roepman, R., Dietmann, S., Virta, M., Kengara, F., Zhang, Z., Zhang, Lifu, Zhao, T., Dai, J., Yang, J., Lan, L., Luo, M., Liu, Z., An, T., Zhang, B., He, X., Cong, S., Liu, Xiaohong, Zhang, W., Lewis, J. P., Tiedje, J. M., Wang, Q., An, Z., Wang, Fei, Zhang, Libo, Huang, T., Lu, C., Cai, Z., Wang, Fang, and Zhang, J. 2021. “Artificial Intelligence: A Powerful Paradigm for Scientific Research,” Innovation (2:4).
Yas, Q., Alazzawi, A., and Rahmatullah, B. 2023. “A Comprehensive Review of Software Development Life Cycle Methodologies: Pros, Cons, and Future Directions,” Iraqi Journal for Computer Science and Mathematics (4:4), pp. 173–190.
Published
2024-04-04
How to Cite
Giffari, R., Ridho, M. M., Indra Sensuse, D., Sumirat Hidayat, D., & Hakiki Purwaningsih, E. (2024). Analyst’s Perception on the Use of AI-based Tools in the Software Development Life Cycle. Jurnal Sistem Informasi, 20(1), 73–87. https://doi.org/10.21609/jsi.v20i1.1399