###### Abstract  
Effort estimation is critical in software engineering to assess the resources needed for development tasks and to enable realistic commitments in agile iterations. This study investigates whether generative AI tools, which are transforming various aspects of software development, can improve effort estimation efficiency. A controlled experiment was conducted in which development teams upgraded an existing information system, with the experimental group using the generative-AI-based tool GitLab Duo for estimation and the control group using conventional methods (e.g., planning poker or analogy-based planning). Results show that while generative-AI-based estimation tools achieved only 16% accuracy--currently insufficient for industry standards--they offered valuable support for task breakdown and iteration planning. Participants noted that a combination of conventional methods and AI-based tools could offer enhanced accuracy and efficiency in future planning. software engineering; agile development; iteration planning; effort estimation; generative AI; tool accuracy +
Footnote †: journal: applied sciences  
0000-0001-4120-6600]V.V.S. notoriously difficult\", which is the result of several interconnected aspects: the high level of complexity and uncertainty in software projects, the overly optimistic nature of software developers and their managers and frequent unexpected technical issues, combined with changing requirements and integration challenges. However, effort estimation in software projects is a well-developed area, full of proven techniques and tools that can be used for accurate estimates [4; 5; 6; 7; 8; 9; 10]. The authors include and explain expert judgement, analogy-based methods, use case points, linear regression, robust regression, neural nets, constructive agile estimation algorithms, COSMIC FP, silent grouping, Delphi methods, COCOMO methods, planning poker and others. The mentioned methods [4; 5; 6; 7; 8; 9; 10] are either time-consuming or fine-tuned for a particular project and/or development team. A novel approach to effort estimation that is based on generative AI is also emerging. Software development platforms introduced this possibility without fully investigating its potentials and drawbacks (please see Section 3). As opposed to rigorous effort estimation in conventional development methods, modern methods of agile software development usually take a rather different approach. Some just-in-time-oriented methods (e.g., Kanban) just eliminated the need for a systematic effort estimation during the development of system maintenance. Other iterative and requirement-driven methods do not employ effort estimation to estimate project duration; they rather estimate fine-grained assets frequently (usually user stories). This is the case with the leading methods of agile development [11], e.g., Scrum [12], Scrumban [13], large scale Scrum (LeSS) [14] and development level of scaled agile framework [15]. The approach was also introduced decades ago in agile development methods such as extreme programming (XP) [16] and feature-driven development (FDD) [17]. The aim of such estimation is to give a fair and sincere commitment for an iteration scope, which is usually two weeks long. Decision-making personnel, managers, product owners, etc., in modern projects are usually not interested in assessments on requirements; they are rather interested in the current iteration commitment (i.e., the list of requirements that the team is guaranteeing to be delivered by the end of an iteration). This is why hour-based assessments today are not the only alternative. In addition, development teams use several techniques and tools to carry out the short-term estimations, including story-points, T-shirt size estimation, planning poker and others [11; 18]. The usual expert-based methods for effort estimation (including planning poker) are two-fold.