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Software Estimation Framework for Digital Enhancements and Maintenance Projects

Software Estimation Framework for Digital Enhancements and Maintenance Projects

Shailesh Kumar Shivakumar
Copyright: © 2020 |Volume: 8 |Issue: 2 |Pages: 16
ISSN: 2643-8089|EISSN: 2643-8097|EISBN13: 9781522583493|DOI: 10.4018/IJPMPA.2020070105
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MLA

Shivakumar, Shailesh Kumar. "Software Estimation Framework for Digital Enhancements and Maintenance Projects." IJPMPA vol.8, no.2 2020: pp.81-96. http://doi.org/10.4018/IJPMPA.2020070105

APA

Shivakumar, S. K. (2020). Software Estimation Framework for Digital Enhancements and Maintenance Projects. International Journal of Project Management and Productivity Assessment (IJPMPA), 8(2), 81-96. http://doi.org/10.4018/IJPMPA.2020070105

Chicago

Shivakumar, Shailesh Kumar. "Software Estimation Framework for Digital Enhancements and Maintenance Projects," International Journal of Project Management and Productivity Assessment (IJPMPA) 8, no.2: 81-96. http://doi.org/10.4018/IJPMPA.2020070105

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Abstract

Software enhancements and the maintenance phase is generally the crucial phase of a software application lifecycle. The enhancements and maintenance consume about 20% of the overall software lifecycle effort. Enhancement and maintenance phase of modern digital projects involves many activities such as incident management, application enhancements, generic maintenance, quality improvements such as automation, preventive maintenance, continuous improvement, and such. State-of the-art estimation models and frameworks fall short of factoring all the dynamics involved in the enhancements and maintenance phase. The article proposes a digital project maintenance estimation framework to estimate various activities of a digital maintenance project. The proposed estimation framework provides comprehensive coverage of maintenance activities including incident management, application enhancements, generic maintenance, and quality improvements. The proposed estimation framework was used to predict effort estimate of 5 digital maintenance projects with MMRE of 0.255 and predicted (0.3) of 80%.

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