Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
Forensic analysis of failed software projects can aid in managerial understanding of the issues and challenges of delivering a successful project. The factors and their interrelationships causing software project failure are not well understood or researched with a strong forensic-analytic approach. Previous papers have not adequately explored how dynamic interaction of multiple factors can lead to critical events that ultimately portend eventual failure. This paper proposes the development of a System Dynamics (SD) model that will represent the key factors, their dynamic interactions, and the influence of exogenous events in causing software project failure. Forensic data will be used as inputs to the SD model to assist managers in understanding the factor interactions, the importance of individual factor metrics, as well as the sequence of interactions in causing possible software project failure. Outcomes from the model will include a likelihood of software project failure, possible factor sequences leading to failure, and suggestions of remediation activities that might mitigate eventual failure.
Applying Forensic Analysis Factors to Construct a Systems Dynamics Model for Failed Software Projects
Online
Forensic analysis of failed software projects can aid in managerial understanding of the issues and challenges of delivering a successful project. The factors and their interrelationships causing software project failure are not well understood or researched with a strong forensic-analytic approach. Previous papers have not adequately explored how dynamic interaction of multiple factors can lead to critical events that ultimately portend eventual failure. This paper proposes the development of a System Dynamics (SD) model that will represent the key factors, their dynamic interactions, and the influence of exogenous events in causing software project failure. Forensic data will be used as inputs to the SD model to assist managers in understanding the factor interactions, the importance of individual factor metrics, as well as the sequence of interactions in causing possible software project failure. Outcomes from the model will include a likelihood of software project failure, possible factor sequences leading to failure, and suggestions of remediation activities that might mitigate eventual failure.
https://aisel.aisnet.org/hicss-55/da/big_data_and_analytics/4