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In the literature, a number of performance-influence modeling approaches have been proposed, which model a configuration option\u2019s influence and a configuration\u2019s performance as a scalar value. However, these point estimates falsely imply a certainty regarding an option\u2019s influence that neglects several sources of uncertainty within the assessment process, such as (1) measurement bias, choices of model representation and learning process, and incomplete data. This leads to the situation that different approaches and even different learning runs assign different scalar performance values to options and interactions among them. The true influence is uncertain, though. There is no way to quantify this uncertainty with state-of-the-art performance modeling approaches.<\/jats:p><jats:p>We propose a novel approach, P4, which is based on probabilistic programming, that explicitly models uncertainty for option influences and consequently provides a confidence interval for each prediction alongside a scalar. This way, we can explain, for the first time, why predictions may be erroneous and which option\u2019s influence may be unreliable. An evaluation on 13 real-world subject systems shows that P4\u2019s accuracy is in line with the state of the art while providing reliable confidence intervals, in addition to scalar predictions. We qualitatively explain how uncertain influences of individual options and interactions cause inaccurate predictions.<\/jats:p>","DOI":"10.1007\/s10664-022-10250-2","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T11:14:46Z","timestamp":1674126886000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mastering uncertainty in performance estimations of configurable software systems"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5562-0894","authenticated-orcid":false,"given":"Johannes","family":"Dorn","sequence":"first","affiliation":[]},{"given":"Sven","family":"Apel","sequence":"additional","affiliation":[]},{"given":"Norbert","family":"Siegmund","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"10250_CR1","doi-asserted-by":"publisher","unstructured":"Aken Dana Van, Pavlo Andrew, Gordon Geoffrey J, Zhang Bohan (2017) Automatic database management system tuning through large-scale machine learning. 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