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However, studies have also shown that traceability practices are still not prevalent in industrial settings due to the high manual effort and lack of tool support. In this paper, we explore the feasibility of using eye tracking to automatically detect trace links to reduce manual effort and thereby increase practical applicability. We conducted a two-stage interview study in industry. In Stage 1 we interviewed 20 practitioners to provide an overview of how traceability is established in practice and how an eye tracking approach would need to be applied in order to be useful. In Stage 2 we conducted interviews with 16 practitioners from one project context to elicit role-specific workflows and analyzed which activities are suitable to obtain useful traceability links based on gaze data. As there is no one-fits-all solution to traceability, and technical limitations of eye tracking still exist, we collected information on used artifact types, tools and requirements management practices to adjust an approach to actual traceability stakeholders\u2019 needs. We report on perspectives from different roles in software projects and give an overview of traced artifacts, current traceability experiences, as well as benefits and doubts concerned with using eye tracking to obtain links automatically. We discuss the implications for the evaluation and implementation of an automatic tracing approach in practice and how eye tracking can support requirements engineering activities.<\/jats:p>","DOI":"10.1007\/s00766-024-00419-0","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T15:22:41Z","timestamp":1720538561000},"page":"329-355","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["What you see is what you trace: a two-stage interview study on traceability practices and eye tracking potential"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9577-0598","authenticated-orcid":false,"given":"Maike","family":"Ahrens","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5003-2340","authenticated-orcid":false,"given":"Lukas","family":"Nagel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7456-8323","authenticated-orcid":false,"given":"Kurt","family":"Schneider","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"419_CR1","doi-asserted-by":"crossref","unstructured":"Mills C, Escobar-avila J, Haiduc S (2018) Automatic traceability maintenance via machine learning classification. 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