TY - GEN
T1 - Testing-based Model Learning Approach for Legacy Components
AU - Ali, Shahbaz
AU - Sun, Hailong
AU - Zhao, Yongwang
AU - Akram, Naveed
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/13
Y1 - 2019/3/13
N2 - Operating, maintaining, and upgrading legacy systems are the foremost challenges which are being faced by many organizations today. Usually, these systems are based on outdated technologies, have limited documentation, and actual developers are unavailable. It is risky to upgrade black-box legacy systems without knowing their internal structures. In this paper, we have proposed an approach which is based on the state of the art dynamic analysis technique known as Model Learning, a reverse engineering approach, to infer the behavioral models of legacy systems. We prepared and utilized our test-bed for black-box vending machines (considered as legacy systems) to learn the behavioral models of all the software modules embedded in vending machines. The in-depth analysis of learned models is helpful in the operation, up-gradation, and maintenance of the legacy system. The experimental results reveal that our proposed approach is very auspicious to modernize the legacy components and explore the concealed structures of the black-box systems automatically.
AB - Operating, maintaining, and upgrading legacy systems are the foremost challenges which are being faced by many organizations today. Usually, these systems are based on outdated technologies, have limited documentation, and actual developers are unavailable. It is risky to upgrade black-box legacy systems without knowing their internal structures. In this paper, we have proposed an approach which is based on the state of the art dynamic analysis technique known as Model Learning, a reverse engineering approach, to infer the behavioral models of legacy systems. We prepared and utilized our test-bed for black-box vending machines (considered as legacy systems) to learn the behavioral models of all the software modules embedded in vending machines. The in-depth analysis of learned models is helpful in the operation, up-gradation, and maintenance of the legacy system. The experimental results reveal that our proposed approach is very auspicious to modernize the legacy components and explore the concealed structures of the black-box systems automatically.
KW - Active automata learning
KW - Learning algorithms
KW - Legacy components
KW - Model learning
KW - Testing and formal verification
UR - https://www.scopus.com/pages/publications/85064112177
U2 - 10.1109/IBCAST.2019.8667149
DO - 10.1109/IBCAST.2019.8667149
M3 - 会议稿件
AN - SCOPUS:85064112177
T3 - Proceedings of 2019 16th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2019
SP - 597
EP - 603
BT - Proceedings of 2019 16th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2019
Y2 - 8 January 2019 through 12 January 2019
ER -