Authors:
Sayandeep Mitra
;
Kritika Anand
and
Pavan Kumar Chittimalli
Affiliation:
Tata Research Development and Design Centre, India
Keyword(s):
Business Rules, Verification, Directed Graph, SMT, Clustering, SBVR, Anomalies.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Model Driven Architectures and Engineering
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Software Engineering
;
Web Information Systems and Technologies
Abstract:
In modern times, business rules have grown exponentially with enterprises becoming more complex in diverse
fields. Due to this growth, different forms of anomalies creep into the business rules, causing business enterprise
to take wrong decisions, which can impact it’s performance and reputation. It is time and resource
consuming to examine the rules manually due to the large number of rules intermingled with each other. The
process of manual verification is also not free of human induced errors. Thus, automatic verification of business rules
is the need of the hour. We present a tool to detect different anomalies in business rules represented in SBVR
format. The tool uses a combination of Directed Graphs and SMT solvers to perform the verification task. We
show the implementation of our tool along with it’s evaluation on industry level benchmarks.