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.

Enterprise Resource Planning (ERP) projects have special characteristics that differentiate them from other kinds of software projects. Main difference that affects effort estimation results is high reuse rates of ERP projects. This paper presents our exploratory work to establish an approach to calculate reuse reflective size of ERP projects, which could be a primary input for effort estimation. We explored the usability of COSMIC function points as the base unit and its convertibility to reuse reflective size using reuse levels. We have performed a case study on an SAP Implementation project. We were able to calculate COSMIC size and reuse levels using the documents available for the project. We discuss the results as well as challenges and opportunities in the light of the case study.

Enterprise Resource Planning (ERP) systems are large scale integrated systems covering most of the business processes of an enterprise. ERP projects differ from software projects with customization, modification, integration and data conversion phases. Most of the time effort and time estimations are performed in an ad-hoc fashion in ERP projects and as a result they frequently suffer from time and budget overruns. Although there is no consensus on a methodology to estimate size, effort and cost of ERP projects there are various research studies in the field. The purpose of this paper is to review the literature on effort estimation methods for ERP projects, their validations and limitations. The systematic literature review used online journal indexes between January 2000 and December 2016. Studies focusing on effort estimation for ERP projects were selected. Two reviewers assessed all studies and 41 were shortlisted. In most of the studies, cost factors for ERP projects were investigated and validated. Our findings showed that effort estimation methods have mostly used function points as an input. Validations of these methods were mostly done by using history-based validation approaches.

Enterprise Resource Planning (ERP) Systems evolve at a rapid pace based on customer and industry expectations. As a result, historic project data for these kinds of projects lose their value especially for analogy based estimation methods. In this rapidly evolving domain, function point based methods might provide a sound alternative for ERP effort estimation. This paper presents the results obtained by applying three methods published in the literature in which function points are used as an input for ERP effort estimation. The evaluation of these methods with respect to their measurement processes and estimation errors are presented; their advantages and disadvantages are discussed.