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Such data may be used to monitor the status of meaningful software indicators (e.g., software quality, productivity and on-time delivery) that are relevant for their decision-making processes. Forecasting the values of such indicators may provide evidence of a potentially high risk or opportunity that could help to anticipate actions accordingly. Most of the existing forecasting proposals in software engineering use open-source data rather than data from industrial projects. Therefore, there is a lack of evidence on how these proposals fit the particular needs of a software-development organization and how they can be automated into the organization\u2019s infrastructure.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>To enable software indicators\u00b4 forecasting in a software-development organization (Modeliosoft).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Method<\/jats:title>\n                <jats:p>We designed an industry-academia collaboration based on Action Design Research (ADR) to address Modeliosoft\u2019s forecasting challenges.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>A tool-supported method called FOSI (Forecasting Of Software Indicators) for enabling forecasting in Modeliosoft. We obtained positive results regarding its suitability and technical feasibility in a pilot project of the organization. In addition, we provide details and reflections on the potential usefulness of the method for addressing similar field problems.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The procedures and results detailed in this paper are valuable to: 1) address Modeliosoft\u2019s forecasting challenges 2) inspire other software-development organizations on how to deal with similar problems and even reuse some procedures and software support tools resulted from this work, 3) promote the win-win benefits of industry-academia collaborations.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s10664-024-10508-x","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T13:45:08Z","timestamp":1727185508000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Forecasting software indicators: an industry-academia collaboration"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6262-3698","authenticated-orcid":false,"given":"Claudia","family":"Ayala","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3872-0439","authenticated-orcid":false,"given":"Cristina","family":"G\u00f3mez","sequence":"additional","affiliation":[]},{"given":"Mart\u00ed","family":"Manzano","sequence":"additional","affiliation":[]},{"given":"Antonin","family":"Abherve","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9733-8830","authenticated-orcid":false,"given":"Xavier","family":"Franch","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"10508_CR1","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1016\/j.jss.2013.03.045","volume":"86","author":"A Amin","year":"2013","unstructured":"Amin A, Grunske L, Colman A (2013) An approach to software reliability prediction based on time series modeling. 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