TITLE:
Process-Oriented Requirements Engineering: User-Centric LORS Framework
AUTHORS:
Hamdan Al-Sabri, Majed Al-Mashari
KEYWORDS:
Business Process Identification/Discovering, Business Process Requirements Engineering, BPMN MIWG, As-Is Business Process Creation, Model Semantics, Model Refinement, LORS Framework
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.10 No.2,
February
7,
2017
ABSTRACT: In recent years, the process orientation requirements engineering field has received significant interest. However, dealing with ordinary users within an enterprise for developing as-is business process is very complex because this requires skills, responsibility, knowledge, and expertise of business processes. This research answers the following questions: 1) how to systematically gather information regarding as-is business process requirements in an informal environment and by non-expert users? and 2) how can an enterprise refine the identified business process with a semantic process model? The primary purpose of this research is to develop a supporting framework that is suitable for the definition of an as-is business process to be deployed within the enterprise environment. Hence, the focus is on gathering requirements to define the as-is business process. This framework concentrates on three significant aspects of this process: development, refinement, and serialization the semantics of the process model. To accomplish this objective, the LORS framework has been proposed, which consists of four phases (List, Order, Refinement, and Serialization). The framework presents a new unique technique to identify the business process, refine the process, and generate the model semantics. This study assumes that a simple and complete framework will help non-expert users define a high-quality as-is process, such that enhance and facilitate the matching process with existing reference models. We present a case study, evaluate the case study relative to specified criteria, and research the limitations and implications discovered from our research. This research concludes that the LORS framework is simple, flexible, visible, interactive, dynamic, and effective.