Over the past decade the traceability research community has focused upon developing and improving trace retrieval techniques in order to retrieve trace links between a source artifact, such as a requirement, and set of target artifacts, such as a set of java classes. In this Trace Challenge paper we present a previously published technique that uses machine learning to trace software artifacts that recur is similar forms across across multiple projects. Examples include quality concerns related to non-functional requirements such as security, performance, and usability; regulatory codes that are applied across multiple systems; and architectural-decisions that are found in many different solutions. The purpose of this paper is to release a publicly available TraceLab experiment including reusable and modifiable components as well as associated datasets, and to establish baseline results that would encourage further experimentation.