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We present one of the University of Kentucky TraceLab components, Similarity Matrix Voting Merge. We highlight some particularly interesting aspects of the component such as challenges faced when developing it. We discuss the challenges encountered when setting up unit testing for the component. We provide an example of the component being used in a TraceLab experiment. We provide a link for download of the component. 

We examine the effects of stemming on the tracing of software engineering artifacts. We compare two common stemming algorithms to each other as well as to a baseline of no stemming. We evaluate the algorithms on eight tracing datasets. We run the experiment using the TraceLab experimental framework to allow for ease of repeatability and knowledge sharing among the tracing community. We compare the algorithms on precision at recall levels of [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], as well as on mean average precision values. The experiment indicated that neither the Porter stemmer nor the Krovetz stemmer outperformed the other on all datasets tested. 

[Context and motivation] Trace matrices are lynch pins for the development of mission- and safety-critical software systems and are useful for all software systems, yet automated methods for recovering trace links are far from perfect. This limitation makes the job of human analysts who must vet recovered trace links more difficult. [Question/Problem] Earlier studies suggested that certain analyst behaviors when performing trace recovery tasks lead to decreased accuracy of recovered trace relationships. We propose a three-step experimental study to: (a) determine if there really are behaviors that lead to errors of judgment for analysts, (b) enhance the requirements tracing software to curtail such behaviors, and (c) determine if curtailing such behaviors results in increased accuracy. [Principal ideas/results] We report on a preliminary study we undertook in which we modified the user interface of RETRO.NET to curtail two behaviors indicated by the earlier work. We report on observed results. [Contributions] We describe and discuss a major study of potentially unwanted analyst behaviors and present results of a preliminary study toward determining if curbing these behaviors with enhancements to tracing software leads to fewer human errors.