## 6 Discussion  
Based on the research questions and empirical data exposed in the previous Section, we can exercise a grounded discussion. The participating developers demonstrated that they mastered the technologies used in the system, and, therefore, they did not suffer a technology-related handicap. They were also already familiar with AI-enabled tools while developing software--but not for effort estimation. However, they were also keen to try the tools for effort estimation. After the first session, the development teams provided task sets and their effort estimates, which enabled us to answer research question **RQ1: Is there a difference between tasks created using conventional and AI-supported tools? ** The answer was positive--both in terms of content and in terms of estimates. **RQ1.1: Is a set of tasks created by generative-AI-supported tools comparable to a set of tasks created using conventional methods? **  
As demonstrated in Section 5, the most obvious difference between the groups is that the experimental group created 260 tasks (which averaged 5.4 tasks per user story), while the control group created only 184 (3.2 tasks per estimated user story). The task volume implies two conclusions:  
* Human-generated tasks are not so fine-grained compared to the AI tool,
* The AI tool can generate more tasks in the same time--which is obviously related to effort estimation, which is time-consuming when carried out manually. Performing a content-related analysis of tasks, we can see that in our experiment, human-generated tasks, in general, are a subset of AI-generated tasks. In general, the control group teams generated tasks that are required explicitly to fulfil the user story. On the other hand, in the experimental group, the teams reported tasks that were general to the development but were not required explicitly (e.g., unit tests, creating/updating documentation, refactoring existing code). Documentation and testing are tasks that can be found in the majority of experimental group task lists, and they add between 2 to 4 h to the estimate per user story. However, we can also see a lot of general tasks in the task lists that are not really related to user stories (e.g., Create a new page template and routing). Based on the analysis of the empirical data, we would encourage development teams to start with their own list of tasks, since human-generated tasks would be more related to the context of the user story. Afterwards, an AI-generated task list can be of great help to see if the developers overlooked some tasks that were not so straightforward to spot. **RQ1.2: Are effort estimations, created by generative-AI-supported tools, comparable to effort estimations created using conventional methods? **  
In addition to the discussion on research question RQ 1.1, the experimental group not only generated considerably more tasks per user story but they were also estimated higher (see Table 1). The conventional effort estimates produced in our experiment were, on average, estimated per user story at 3.37 h, with an average task of 1 h. Contrarily, in the experimental group, we can see the average user story being estimated at 13.5 h, with tasks at an average estimate of 2.5 h. This is why we can see cumulative estimates in the experimental group teams that are considerably higher (108 h on average) compared to the control group (27 h)--see Figure 6.  
Speaking strict statistics, when we compared the estimates of the same user stories by the experimental and control groups, the estimates were proven to have significant differences (two-tailed student \(t\)-test resulted in a \(p\)-value of 3.03 \(\times\) 10\({}^{-7}\), \(t\)-value 6.676--see Table 3). By analysing the tasks and their estimates closely, we can spot several possible causes for the really high difference between the estimates. Related to the discussion of research question RQ 1.1, an important number of estimates was added by tasks that the teams in the control group did not envision (testing, documentation, refactoring).