## 3 Related Work  
The last five years systematic review study [23] did not capture any generative-AI-based approaches or tools. The aim of our research is to verify how generative-AI-based methods perform in real-world scenarios. The same applies to another review research [18], where generative-AI-based effort estimation was not identified. However, we can find research in the field of employing generative AI for effort estimation. In one, the authors [25] describe the conceptual identification of the capabilities of LLM for cost estimation. They used a COSMIC function point estimation model as a prototype. An experiment that they conducted gives a comparison of a manual measurement with trained and untrained models. Their reported findings show that the cost estimation process can be accelerated by using large scale models. They showed how COSMIC function point mapping and measurement can be automated. Another niche study, employing generative AI in effort estimation [26], deals with intelligent interfaces and their user experience. They propose effort estimation based on generative AI in the form of a case study. The limitation of this study is that it is not generic to the field of effort estimation; it is focused towards enhancing the COSMIC function point model. [PERSON] et al. [27] introduced a conceptual solution designed to improve estimation accuracy and efficiency while minimising manual effort and cost. Their approach involved extracting entities and their relationships from the system, modelling them as semantic triples and developing conceptual micro-services. To enable a more precise functionality breakdown and more accurate effort estimation during microservice development, they incorporated prompt engineering with ChatGPT, enhancing requirements understanding, which, in turn, improved the accuracy of effort estimation. Generative AI in this case was not employed directly to perform the estimations. A web-based AI chatbot named Alfred is a solution that the authors [28] introduced in their paper. It aids agile software release planning by employing two machine learning models to estimate the duration of tasks and recommend optimal resource assignments for project managers. It is also able to categorise the task estimates based on their confidence level into three groups: low, medium, or high. Chatbot in this case is used as a user-friendly interface for underlying machine learning models. [PERSON] and [PERSON] and [PERSON] (2019) explored the bigger picture of project management in their research paper. They compared GPT-4 and a human project manager in developing project plans, analysing aspects like scope and schedule, as well as cost (effort) and resource estimation, which touched upon our research topic. The study found that AI and human plans have complementary strengths and weaknesses, and it highlighted the importance of human expertise in refining AI outputs. In this sense, the paper outlined similar takeaways as our study. However, our research was focused on task effort estimation carried out by developers using specialised tools. The potential of generative AI in terms of planning iterations and estimating effort is also articulated clearly in the latest Gartner Research Reports (2019). Among others, they claim that by 2027, the number of platform engineering teams using AI to augment every phase of the SDLC will have increased from 5% to 40%. In addition, they reported in 2024 a list of the state-of-the-art automation tools available in certain points in the DevOps cycle. For planning the iteration based on generative AI, they identified the following tools and providers: Atlassian Intelligence, ClickUp, GitLab Duo and OpenText. Based on their recommendation and our own preliminary tool searching and testing, we decided to use GitLab Duo, which is integrated into GitLab Enterprise. Being listed as one of the leading tools, it gave us the confidence to generalise the findings of our research.