Automated Software Engineering Process Assessment: Supporting Diverse Models using an Ontology

Current software engineering process assessment reference models rely primarily on manual acquisition of evidence of practices. This manually collected data is then correlated with expected model attributes to assess compliance. Such manual data acquisition is inefficient and error-prone, and any assessment feedback is temporally detached from the original context by months or years. Yet in order to automate the process data acquisition and assessment, one is confronted with various challenges that such diverse project-specific software engineering environments involve. This paper presents an ontology-based approach for enhancing the degree of automation in current process assessment while simultaneously supporting diverse process assessment reference models (CMMI, ISO/IEC 15504, ISO 9001). It also provides an in-the-loop automated process assessment capability that can help software engineers receive immediate feedback on process issues. The evaluation showed the approach’s technical feasibility, model diversifiability across various process assessment models (CMMI, ISO/IEC 15504, ISO 9001), and suitable performance and scalability. The approach can reduce the effort required to determine process compliance, maturity, or improvement, and can provide more timely and precise feedback compared to current manual process assessment methods and tools.