# Simulating the Software Development Lifecycle: The Waterfall Model
## 4 Results

Table 8: The number of times a delay occurred (Count), as well as the maximum (Max Wait), mean (Mean Wait), and standard deviation (Std. Dev.) of wait times, for resources by size of project. when looking at mean completion times. The values went from 49.995 to 47.298, from 81.944 to 59.950, from 119.643 to 80.827, and from 75.222 to 57.540 for small, medium, large, and across all sizes, respectively. The data clearly indicate that the implementation of a zero-wait strategy can significantly improve project completion times. We also looked at the duration of the individual phases for both Scenario 1 and Scenario 2. In the analysis phase, we see that the minimum completion time remained a constant 3.000 units across all project sizes, irrespective of scenario. Conversely, the maximum time required for completion in Scenario 2 saw a significant reduction, dropping to 5.000 units from as high as 8.189 units of time for large projects. The mean values showed only minor fluctuations across different project sizes, going from 4.083 units of time for Scenario 1 to 4.058 units of time for Scenario 2, thereby revealing that the size of the project does not significantly impact the average duration in the analysis phase. Moving on to the design phase, the data reveal that while the minimum time remained constant at 5.000 units across all sizes and scenarios, the maximum time required saw a substantial reduction post-optimization. This was most pronounced for large projects, where the maximum time was reduced from 18.059 to 10.000 units of time. Similarly, small projects dropped from 15.541 to 10.000 units of time and medium-sized projects from 15.000 to 10.000 units of time. The average durations showed minimal variation regardless of project size, going from 8.255 to 7.558 units of time. During the implementation phase, the minimum time remained consistent at 15.000 units across all project sizes. However, the maximum time saw a drastic reduction for Scenario 2, dropping to 20.000 units from as high as 62.396 units for large projects. Comparably, for small-sized projects the drop was from 61.000 to 20.000 units of time and for medium-sized projects from 48.000 to 19.000 units of time. The mean times dropped from 24.730 to 17.510 units of time. The testing and maintenance phases showed stable minimum and maximum times for the most part. In the case of the testing phase, a drop was seen with respect to large projects and maximum completion times, which dropped from 16.000 units of time for Scenario 1 to 10.000 units of time for Scenario 2. In summary, there was a significant reduction in maximum completion times across all phases for Scenario 2. This supports the conclusion that the zero-wait approach was effective in eliminating bottlenecks. Additionally, while large projects generally take more time for Scenario 1, the zero-wait approach effectively narrows this gap, indicating that such a process can improve worst-case scenarios. Table 10 provides greater detail on the duration of phases by size of project across iterations in both scenarios. The simulations also provided insight into the likelihood of a phase failing within the software development lifecycle. It should be noted that, based on our simulation of the waterfall model, errors can first become possible during the design phase. This is in line with the approach proposed by [PERSON] [21]. Furthermore, we should highlight that the probabilities of an error occurring did not vary between the scenarios, thus having no effect on the possibility of failure. The likelihood of failure was based exclusively on project size. We should also share that our software does allow differentiation by phase should that be desired, namely analysis, design, implementation, testing, and maintenance. The failure rate for small projects was 5.323 percent for Scenario 1 and 7.715 percent for Scenario 2;