All stakeholders have full confidence in the traceability, as it is created and maintained in the face of inconsistency, omissions and change; they can and do depend upon it.
In the vision scenario, the engineer is confident to make decisions based upon the options presented to her. She trusts the results of the traceability and expects the associated analyses it enables to be accurate and up to date at all times. The engineer is alerted to the impact on traceability of potential changes in the requirements and their implementation, and any requisite traceability updates for the changes that are implemented are made proactively, meaning that this confidence in this traceability is retained. The traceability simply self-repairs and evolves at all times without the engineer having to do anything explicit. The engineer is also comfortable to delegate any ensuing tasks that will impact the traceability, as she trusts that the overall traceability will not be jeopardized by others
The traceability that is established on many projects often has a dubious provenance, impacting how much trust can be placed in the analyses it facilitates, as well as its longevity. People establishing traceability make mistakes that go undetected and the impact of such mistakes are rarely known. Traces decay unless they are attended to, but the useful life and quality of the trace links is usually unknown. The traced artifacts can also expire and this can remain unknown, with unforeseeable consequences. Without effort, there is traceability entropy over time. This is a vicious cycle for both establishing and using traceability
Perform quality assessment and assuring the traceability.
Develop a model of the vulnerabilities in the traceability process, including human error in both manual and automated approaches, and develop requisite techniques to bolster their reliability.
Formulate metrics for traceability quality assessment, especially for automatically created and maintained traces. Precision and recall metrics are a start.
Create a visual dashboard for displaying and examining traceability quality attributes on a project.
Gain improvements in the quality of both manual and automatically created and maintained trace links.
Advance the run-time monitoring of traceability quality with validated error detection models for trace links.
Apply concepts from autonomic computing to explore self-healing traceability techniques and methods, covering diagnosis, repair actions and propagation, to apply at both the individual trace and collection of traces levels.
Gather case study evidence as to the quality of traceability techniques and methods within the Traceability Body of Knowledge.
Catalogue quality requirements with respect to the traceability for supporting different end-user tasks within the Traceability Body of Knowledge.
Provide ways of inferring trust in the traceability based on how the trace links are established and used, and by whom.
Practitioners consult and contribute to an evolving Traceability Body of Knowledge.