A Requirement-centric Framework to Build Tool Integration for Traceability and Consistency

Abstract

Many automotive companies use the ISO 26262 and Automotive SPICE (ASPICE) standards to develop software. These standards highlight the importance of traceability and consistency. However, in practice, different companies use various software tools for different tasks such as software testing and requirement management. Current methods to achieve traceability often depend heavily on specific suppliers or tools. Some methods require a lot of manual work, like maintaining Excel sheets, while others automate the process but are not efficient enough and stick to certain standards. The issue of ensuring consistency has received less attention. A more generic and flexible approach is needed to establish traceability and identify inconsistencies using data, not relying on specific tools. This thesis study was conducted at Volvo Car Corporation with a software integration and release team. Not just this team, but other departments also face a challenge: there is no effective workflow to establish traceability for automated HIL (Hardware in the Loop) testing. Requirements are managed in a tool called Car- Weaver, while test cases are executed either automatically in dSPACE Automation- Desk or manually in dSPACE ControlDesk. Currently, traceability and visualization are only managed for manual testing using Excel sheets. As automated HIL testing becomes more common, a method to also report the status of requirements is needed. Furthermore, the consistency required by ASPICE is lacking in the current approach. This thesis proposes a requirement-focused framework to create a tool integration framework for establishing traceability and checking consistency. This framework does not depend on any specific software vendor in the automotive industry. It involves collecting data on test results and requirements from various software tools and then processing and analyzing this data centrally. The study provides a practical example, including adjusting the test workflow to capture the right data and using APIs to transform requirement data. The central processing unit uses Python scripts for data handling and Power BI for visualization. This framework could also be useful in other fields where requirement traceability and consistency are issues, especially when different tools are used for various purposes. Design science research is adopted as the research methodology with two cycles of iteration, each involving problem investigation, solution design, design validation, solution implementation, and implementation evaluation.