## 6 Discussion
However, even with those tasks eliminated, we have an average user story estimate over 10 h. Another anomaly that we spotted in the experimental group compared to the control group were tasks that were not really context related, having also quite a high estimate (e.g., user story \"Create a new page template and routing\" was estimated at 2 h, while this task was not required in the project and was therefore not listed in the control group estimates). A third anomaly when comparing task estimates were the same tasks identified by both group teams but estimated higher by the experimental group teams. The reason for this might have been in (over)confident developers or in the AI-tool, which did not take into account the developers' abilities and/or carried out the estimation in a worst-scenario manner. An example is a task from User Story #1 (Email Notification), which demands the developer prepare a custom, project-dedicated email (creating an email in some free online providers). In the control group teams, this task was estimated between a quarter and a half hour, while the AI-tool estimated this task at over 1 h, in the case of one team also 3 h, which was an obvious overestimate. Let us discuss the estimate accuracy that we targeted with research question 2 (**RQ2: How accurate is the effort estimation generated by a generative-AI-supported tool compared to conventional estimation methods?**). In order to address the accuracy, we asked the development teams to perform the actual implementation. The accuracy was then calculated as the ratio between the estimate of task or user story and the actual effort, reported by the developers (see Equation (3)). As stated in Section 5, the developers tracked their development time based on tasks. If a user story was accepted, then the user story's actual effort was calculated as a sum of the fulfilled task efforts (see Equation (2)). \[\text{Estimation Accuracy(US}_{i})=\frac{\text{Actual effort(US}_{i})}{ \text{Estimate(US}_{i})}\times 100\% \tag{3}\]  
After the implementation, 32 user stories were accepted, performed by implementing 101 tasks, which were our empirical data body to carry out the accuracy analysis. The accuracy of the experimental group estimates was really low: 16% on average (standard deviation of 9.2%). This is not an acceptable level of accuracy, as expected by the industry [32]. To put this into perspective, the control group's effort accuracy was 60% (standard deviation 24.5%), which correlates to the expected accuracy in the industry [32]. With this, we answered research question 2 partially (RQ 2: How accurate is the effort estimation generated by an AI-supported tool compared to conventional estimation methods?). The accuracy per user stories and groups is shown in Figure 9.  
Since the estimation accuracy calculation is carried out with two variables (estimated effort, actual effort), a legitimate debate can also question the correctness of the reported actual effort. To minimise the risk of misreporting, as already explained in Section 4, we did the estimates from the development teams before the implementation phase. This approach is not uncommon in agile development, where estimates are primarily used during planning meetings to help teams make fair and informed commitments. During the development phase, these estimates are typically \"hidden\" to ensure developers can focus on delivering high-quality solutions without being influenced by prior estimations, thereby maintaining the integrity of the development process. Another interesting insight, supporting the correctness of reported hours, can be seen in Table 2 of Section 5: regardless of the different task lists and estimates, the development teams of both groups demonstrated similar efforts for implementing user stories to an acceptable level (2 and 1.92 h). All the estimates and actual efforts of both groups per user story are shown in Figure 10, where we can see clearly the similarities between the control group estimates, control group actual effort and experimental group actual effort.