TY - GEN
T1 - Detecting Bottleneck and Fraud in Agile Development by using Petri net Performance and Trace Clustering
AU - Razi, Muhammad Ar
AU - Sungkono, Kelly Rossa
AU - Sarno, Riyanarto
AU - Wahyuni, Cahyaningtyas Sekar
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Software Development Life Cycle model is one of the basic idea of the software development. SDLC is a continuous process, the cycle starts from the moment when it is decide to release the project, and it ends when its full remove from the exploitation. Agile is one of the mostly used SDLC models. Combination of incremental and iterative process models with a focus on process customer satisfaction and can adapt to rapid delivery of working sofware is called Agile SDLC. This contribution of this paper is evaluating the performance, such as occurring bottleneck and fraud, of an Agile model to handle the management of the project. This paper uses Petri net to analyze Agile SDLC model performance. The petri net use to detect the bottleneck that occur in the model. If an event has longer waiting time tahen others, it can identify that the bottleneck occur in that task. Then, the fraud is detected based on trace clustering. Based on the experiment, the cluster which has lowest member is indicates as fraud processes.
AB - Software Development Life Cycle model is one of the basic idea of the software development. SDLC is a continuous process, the cycle starts from the moment when it is decide to release the project, and it ends when its full remove from the exploitation. Agile is one of the mostly used SDLC models. Combination of incremental and iterative process models with a focus on process customer satisfaction and can adapt to rapid delivery of working sofware is called Agile SDLC. This contribution of this paper is evaluating the performance, such as occurring bottleneck and fraud, of an Agile model to handle the management of the project. This paper uses Petri net to analyze Agile SDLC model performance. The petri net use to detect the bottleneck that occur in the model. If an event has longer waiting time tahen others, it can identify that the bottleneck occur in that task. Then, the fraud is detected based on trace clustering. Based on the experiment, the cluster which has lowest member is indicates as fraud processes.
KW - Agile
KW - Alpha algorithm
KW - Bottleneck
KW - Petri net
KW - SDLC
UR - http://www.scopus.com/inward/record.url?scp=85074915283&partnerID=8YFLogxK
U2 - 10.1109/ISEMANTIC.2019.8884226
DO - 10.1109/ISEMANTIC.2019.8884226
M3 - Conference contribution
AN - SCOPUS:85074915283
T3 - Proceedings - 2019 International Seminar on Application for Technology of Information and Communication: Industry 4.0: Retrospect, Prospect, and Challenges, iSemantic 2019
SP - 214
EP - 218
BT - Proceedings - 2019 International Seminar on Application for Technology of Information and Communication
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Seminar on Application for Technology of Information and Communication, iSemantic 2019
Y2 - 21 September 2019 through 22 September 2019
ER -