## 5 Results
### Task Generation and Effort Estimation Phase  
The participating development teams were given the eight user stories and were asked to prepare a sound set of tasks and their effort estimates. The development teams  
Figure 4: Participant experience. Figure 5: How do you think the use of AI tools helps in software development? generated 260 (experimental group) and 184 tasks (control group). As seen in Table 1, the development teams in the experimental group generated 43 tasks on average, while the control group development teams provided 29. An interesting number in Table 1 is also the average task and user story estimate. The user story estimate is calculated as a sum of all task estimates that belong to the user story (see Equation (1)). \[\text{Estimate(US}_{i})=\sum_{\text{US};\text{tasks}}Estimate(task) \tag{1}\]  
In the experimental group, the average task effort estimate is 2.5 h, while in the control group, the developers estimated tasks on average at less than half of that: 1 h. This led us to an average user story estimate that was, in the case of the experimental group, 13.5 h and, in the case of the control group, a quarter of that: 3.4 h.  
An additional insight into effort estimates is given in Figures 6 and 7. Figure 6 shows the cumulative hours estimated by the experimental group teams (EG1-EG6) and the control group teams (CG1-CG7). We can see that the teams in the experimental group estimated significantly more effort (on average 108 h per team) compared to the control group teams (average 27 h). Another perspective on cumulative effort estimates is presented in Figure 7. We can see that the user story average effort estimates (all the development teams) are considerably higher in all cases for the experimental group. Let us demonstrate the difference in task content and individual effort estimates in the case of User Story #2 (Monthly reporting), with representative examples from the development teams in control in the experimental groups. The tasks generated by development team 6 (summarising in 6 h of estimate for User Story #2) in the control group were as follows:  
* Monthly report button--0.5 h
* New Table--2 h
* Backend--review of all measurements within 30 days--0.5 h
* Backend--capturing measurements outside the tolerances--0.5 h
* Frontend--arrangement of the covered measurements descending--1 h
* Frontend--show inadequate measurements--1.5 h \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Experimental Group** & **Control Group** \\ \hline Development teams & 6 & 7 \\ \hline Estimated user stories per development team & 8 & 8 \\ \hline Estimated user stories & 48 & 56 \\ \hline Generated and estimated development tasks & 260 & 184 \\ \hline Tasks per development team & 43.33 & 26.29 \\ \hline Task estimate & average [h] & 2.50 & 1.03 \\  & st. dev & 1.33 & 0.79 \\ \hline User story estimate & average [h] & 13.55 & 3.37 \\  & st. dev & 6.02 & 1.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of generated tasks and estimation summary. While the development teams in the control group were given freedom on creating and estimating tasks (except for AI-tool support), the experimental group development teams had to use Gitlab Duo. They had to report not only the resulting tasks and estimates but also the prompts. Let us see some task sets for the same user story that demonstrate how a prompt plays an important role; we can also see that effort estimates are considerably higher. Development team 1 used the following prompt to obtain the task set, which summarises a 14 h estimate for User Story #2, more than double compared to the control group development team:  
_Based on the existing code, write me tasks (and their time estimates in hours) to implement the following requirement: 1 need a new web page that will display a monthly report on how many times a temperature has been out of the tolerance for each product. The data must be displayed in descending order.