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A Lightweight Incremental Effort Estimation Model for Use Case Driven Projects
Use case analysis has been widely adopted in modern software engineering due to its strength in elaborating functional requirements. It is often done with a UML use case model that formalizes interactions between actors and the system in the requirements elicitation iteration, and with architectural alternatives explored and user interface details specified in the following analysis and design iteration. To better support decision making in resource allocation and planning, it is required for effort estimation models to provide estimates about the total required effort at the very early stage of a project, which, however, provides little information for evaluating system complexity. To solve this dilemma, an incremental approach of integrating information available throughout the early iterations for multiple effort estimations is preferred in keeping the balance between the utility and accuracy. In this paper, we proposed an effort estimation model that incorporates two sub-models to provide two points of effort estimation during the early iterations of a use case driven project. To be more specific, the early-phase model is defined based on a size metric called Early Use Case Point (EUCP), which weights a use case by the logical complexity conveyed within structured scenarios, while the later-phase model is defined based on the size metric called Extended Use Case Point (EXUCP), which takes into consideration data complexity and UI complexity when evaluating transactional complexity. Our proposed model is lightweight due to the fact that its size metrics are defined to be countable directly from the artifacts of the two iterations. To better calibrate the model, especially in considering the situation of having limited data points available, we also introduced a normalization framework in our model calibration process to reduce noise within the effort data. By calibrating the proposed sub-models with the data points collected from 3 historical projects, we demonstrated the goodness of fit of the sub-models to the dataset and the superiority of the early-phase model over the later-phase model in terms of smaller standard errors of the calibrated parameters.
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Kan Qi (Primary Presenter,Author), University of Southern California, email@example.com;
Kan Qi is a Ph.D. student from the Computer Science Department at University of Southern California under the supervision of Prof. Barry Boehm. He received his B.S. in computer science from Changchun University in 2013 and his M.S. degree in computer science from USC in 2015. His research interests are in software metrics, cost estimation, model-based analysis, process improvement, and agile development.
Barry Boehm (Author), USC, firstname.lastname@example.org;
Dr. Barry Boehm is the TRW Professor in the USC Computer Sciences, Industrial and Systems Engineering, and Astronautics Departments. He is also the Director of Research of the DoD-Stevens-USC Systems Engineering Research Center, and the founding Director of the USC Center for Systems and Software Engineering. He was director of DARPA-ISTO 1989-92, at TRW 1973-89, at Rand Corporation 1959-73, and at General Dynamics 1955-59. His contributions include the COCOMO family of cost models and the Spiral family of process models. He is a Fellow of the primary professional societies in computing (ACM), aerospace (AIAA), electronics (IEEE), and systems engineering (INCOSE), and a member of the U.S. National Academy of Engineering.