Vetting automatically generated trace links: What information is useful to human analysts?

Paper in proceeding, 2018

Automated traceability has been investigated for over a decade with promising results. However, a human analyst is needed to vet the generated trace links to ensure their quality. The process of vetting trace links is not trivial and while previous studies have analyzed the performance of the human analyst, they have not focused on the analyst's information needs. The aim of this study is to investigate what context information the human analyst needs. We used design science research, in which we conducted interviews with ten practitioners in the traceability area to understand the information needed by human analysts. We then compared the information collected from the interviews with existing literature. We created a prototype tool that presents this information to the human analyst. To further understand the role of context information, we conducted a controlled experiment with 33 participants. Our interviews reveal that human analysts need information from three different sources: 1) from the artifacts connected by the link, 2) from the traceability information model, and 3) from the tracing algorithm. The experiment results show that the content of the connected artifacts is more useful to the analyst than the contextual information of the artifacts.