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In this study, we used a technology transfer model to guide the adoption of an ML technique at a company for the early identification of invalid bug reports. In the process, we also identify necessary conditions for adopting such techniques in practice. We followed a case study research approach with various design and analysis iterations for technology transfer activities. We collected data from bug repositories, through focus groups, a questionnaire, and a presentation and feedback session with an expert. As expected, we found that an ML technique can identify invalid bug reports with acceptable accuracy at an early stage. However, the technique\u2019s accuracy drops over time in its operational use due to changes in the product, the used technologies, or the development organization. Such changes may require retraining the ML model. During validation, practitioners highlighted the need to understand the ML technique\u2019s predictions to trust the predictions. We found that a visual (using a state-of-the-art ML interpretation framework) and descriptive explanation of the prediction increases the trustability of the technique compared to just presenting the results of the validity predictions. We conclude that trustability, integration with the existing toolchain, and maintaining the techniques\u2019 accuracy over time are critical for increasing the likelihood of adoption.<\/jats:p>","DOI":"10.1007\/s10664-024-10502-3","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T12:03:07Z","timestamp":1722427387000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Industrial adoption of machine learning techniques for early identification of invalid bug reports"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5964-5554","authenticated-orcid":false,"given":"Muhammad","family":"Laiq","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nauman bin","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00fcrgen","family":"B\u00f6rstler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emelie","family":"Engstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"issue":"5","key":"10502_CR1","doi-asserted-by":"publisher","first-page":"3544","DOI":"10.1007\/s10664-020-09846-3","volume":"25","author":"EU Aktas","year":"2020","unstructured":"Aktas EU, Yilmaz C (2020) Automated issue assignment: results and insights from an industrial case. 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