TY - GEN
T1 - Scenario-Based Requirement Engineering
T2 - 2025 International Conference on Electrical Engineering and Information Systems, CEEIS 2025
AU - Sandfreni, Sandfreni
AU - Budiardjo, Eko K.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The evolution of computing technology has created several new avenues for artificial intelligence to become part of various industries. The most important drawback in AI systems, especially for the financial sector, is the difficulty in interpreting complex and diverse requirements. This study introduces the application of scenario-based requirements engineering (SBRE) offering a comprehensive methodology that addresses this challenge for developing AI-based credit decision systems. SBRE offers a novel approach in handling the complexity of AI-based systems that is adaptive to changing data and operating conditions. Unlike traditional approaches, this study integrates dynamic scenarios for validation and verification of requirements, which ultimately improves the credit model accuracy, reduces risks, and ensures requirements are met. This study makes a significant contribution in expanding the scope of requirements engineering for AI-based systems and paves the way for further exploration in other sectors.
AB - The evolution of computing technology has created several new avenues for artificial intelligence to become part of various industries. The most important drawback in AI systems, especially for the financial sector, is the difficulty in interpreting complex and diverse requirements. This study introduces the application of scenario-based requirements engineering (SBRE) offering a comprehensive methodology that addresses this challenge for developing AI-based credit decision systems. SBRE offers a novel approach in handling the complexity of AI-based systems that is adaptive to changing data and operating conditions. Unlike traditional approaches, this study integrates dynamic scenarios for validation and verification of requirements, which ultimately improves the credit model accuracy, reduces risks, and ensures requirements are met. This study makes a significant contribution in expanding the scope of requirements engineering for AI-based systems and paves the way for further exploration in other sectors.
KW - AI system
KW - artificial intelligence (AI)
KW - requirement engineering (RE)
KW - scenario-based requirement engineering (SBRE)
UR - https://www.scopus.com/pages/publications/105011066256
U2 - 10.1109/CEEIS65979.2025.00009
DO - 10.1109/CEEIS65979.2025.00009
M3 - Conference contribution
AN - SCOPUS:105011066256
T3 - Proceedings - 2025 International Conference on Electrical Engineering and Information Systems, CEEIS 2025
SP - 1
EP - 7
BT - Proceedings - 2025 International Conference on Electrical Engineering and Information Systems, CEEIS 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 February 2025 through 2 March 2025
ER -