An Empirical Study on Requirements Traceability Using Eye-Tracking. Ali, N., Sharafi, Z., Gu�h�neuc, Y., & Antoniol, G. In Di Penta, M. & Maletic, J. I., editors, Proceedings of the 28<sup>th</sup> International Conference on Source Maintenance (ICSM), pages 191–200, September, 2012. IEEE CS Press. 10 pages.
An Empirical Study on Requirements Traceability Using Eye-Tracking [pdf]Paper  abstract   bibtex   
Requirements traceability (RT) links help developers understand programs and ensure that their source code is consistent with its documentation. Creating RT links is a laborious and resource-consuming task. Information Retrieval (IR) techniques are useful to recover automatically traceability links but IR-based approaches typically have low accuracy (precision and recall) and, thus, creating RT links remains a human intensive process. We conjecture that understanding how developers create RT links could help improving the accuracy of IR-based approaches to recover RT links. Consequently, we perform an empirical study consisting of two controlled experiments. First, we use an eye-tracking system to capture developers' eye movements while they verify RT links. We analyse the obtained data to identify and rank developers' preferred source code entities (SCEs), \eg class names, method names, used by these developers. Second, we use the ranked SCEs to propose two new weighting schemes called $SCE/IDF$ and $DOI/IDF$ to recover RT links combined with an IR technique. $SEC/IDF$ is based on the developers preferred SCEs to create RT links. $DOI/IDF$ is an extension of $SEC/IDF$ distinguishing domain and implementation concepts. We use LSI combined with $SEC/IDF$, $DOI/IDF$, and $TF/IDF$ to show, using two systems, iTrust and Pooka, that $LSI_{DOI/IDF}$ statistically improves the accuracy of the recovered RT links over $LSI_{TF/IDF}$.
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