Automating cross-disciplinary defect detection in multi-disciplinary engineering environments

Abstract

Multi-disciplinary engineering (ME) projects are conducted in complex heterogeneous environments, where participants, originating from different disciplines, e.g., mechanical, electrical, and software engineering, collaborate to satisfy project and product quality as well as time constraints. Detecting defects across discipline boundaries early and efficiently in the engineering process is a challenging task due to heterogeneous data sources. In this paper we explore how Semantic Web technologies can address this challenge and present the Ontology-based Cross-Disciplinary Defect Detection (OCDD) approach that supports automated cross-disciplinary defect detection in ME environments, while allowing engineers to keep their well-known tools, data models, and their customary engineering workflows. We evaluate the approach in a case study at an industry partner, a large-scale industrial automation software provider, and report on our experiences and lessons learned. Major result was that the OCDD approach was found useful in the evaluation context and more efficient than manual defect detection, if cross-disciplinary defects had to be handled.