Leveraging AI To Enhance the Software Development Lifecycle

The Software Development Life Cycle (SDLC) has many inefficiencies such asmanual tasks, that often lead to errors, costly rework, delays, and project overruns. While Artificial Intelligence (AI) tools have demonstrated improvements in development workflows, AI integration and adoption remain fragmented and limited to individual phases. This research examines the impact of end-to-end AI integration across all SDLC phases, utilizing a Discrete Event Simulation (DES) model with Monte Carlo sampling. The simulation was run 10,000 times with 100 tasks per run producing task-level observations. The results demonstrated an overall cycle time reduction of 58%, with phase-specific improvements ranging from 12.7% to 68.2%, primarily driven by the adoption rate per phase. The simulation results are compared against published benchmarks from literature to provide context through independently conducted studies. The simulation model serves as a decision tool for software engineering managers for prioritizing and planning AI implementation in their workflows and provides a mechanism for estimating cycle time improvements.

Author

• Gentuba, Joan Roncesvalles

Language

• en

Keyword

• SDLC
• Engineering Management
• Software Engineering
• Artificial Intelligence
• Software Development Life Cycle
• AI

Date created

• 2026

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Mazzuchi, Thomas
• Heine, Karen

Committee Member(s)

• Blackford, Joseph

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/k3569539x