Software Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and Software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.

Software project management plays an important role in producing high-quality software, and effort estimation can be considered as a backbone for successful project management. Size is a very significant attribute of software by being the only input to perform early effort estimation. Even though functional size measurement methods showed successful results in effort estimation of traditional data-centric architectures such as monoliths, they were not designed for today’s architectures which are more service-based and decentralized such as microservices. In these new systems, the event concept is highly used specifically for communication among different services. By being motivated by this fact, in this study, we looked for more microservice-compatible ways of sizing microservices using events and developed a method accordingly. Then, we conducted an exploratory case study in an organization using agile methods and measured the size of 17 Product Backlog Items (PBIs) to assess how this proposed method can be useful in effort estimation in microservices. The implication from the case study is that despite performing a more accurate effort estimation using the proposed size measurement than COSMIC, we were unable to significantly outperform using the total number of events. However, our suggested approach demonstrated to us a different way to use software size in terms of events, namely, to determine the coupling complexity of the project. This finding can be beneficial specifically when evaluating the change requests.

Functional size measurement (FSM) methods, by being systematic and repeatable, are beneficial in the early phases of the software life cycle for core project management activities such as effort, cost, and schedule estimation. However, in agile projects, requirements are kept minimal in the early phases and are detailed over time as the project progresses. This situation makes it challenging to identify measurement components of FSM methods from requirements in the early phases, hence complicates applying FSM in agile projects. In addition, the existing FSM methods are not fully compatible with today’s architectural styles, which are evolving into event-driven decentralized structures. In this study, we present the results of a case study to compare the effectiveness of different size measures: functional -COSMIC Function Points (CFP)-, event-based - Event Points-, and code length-based - Line of Code (LOC)-on projects that were developed with agile methods and utilized a microservice-based architecture. For this purpose, we measured the size of the project and created effort estimation models based on three methods. It is found that the event-based method estimated effort with better accuracy than the CFP and LOC-based methods.

Functional size measurement (FSM) has been used in software engineering for decades as a main driver for estimation and significant input for other various project management activities throughout the project life span. To apply FSM accurately at the early stages of software development process, especially for estimation purposes, functional user requirements need to be available in detail as required by the adopted FSM method. However, in agile software development, requirement specifications, in general, are kept minimal. For this reason, the adjustment of the requirements to the necessary granularity level has been articulated as one of the barriers preventing the diffusion of FSM practices among agile teams. In this paper, we take a closer look at this problem in order to investigate the usability of FSM and to reveal FSM related challenges empirically through case studies on real agile projects from different software organizations. This study also provides a snapshot of agile organizations in terms of requirement specification and estimation related practices.

Agile methodologies have gained significant popularity among software development organizations during the last decade. Although agile methodologies are regarded as minimizing formal processes, they still utilize an estimation methodology for proper management. Story point is the most common input for agile effort estimation. Story point is an arbitrary measure; it reflects experiences of project participants. On the other hand, functional size is an alternative measure used in practice as an input for effort estimation. In this research, we collect and present the outcomes of three case studies which compared the effectiveness of COSMIC-based and story point based effort estimation in agile context. On selected projects of these organizations, software functional size was measured with COSMIC functional size measurement methodology. Effort prediction models were formed by using COSMIC size and actual effort spent; and the models were tested in terms of their effectiveness. The results show controversial outcomes. For all the cases, COSMIC based estimation was more precise. Therefore, COSMIC is an appropriate measure to estimate the effort in organizations that adopt agile software development. It is also observed that COSMIC allowed for computing productivity which has less disperse distribution than the productivity computed with SP. The data is also provided to help other researchers conduct their own studies.