Bibtex
Cite as text
@Inbook{Brodmann+Rodner,
Cite-key = "brodmann2022",
Year= "2022",
Volume= "AKWI-Tagungsband zur 35. AKWI-Jahrestagung",
Pages= "S. 287–303",
Journal = "Monographs",
Title= "OpenPredict - An Open Research Dataset and Evaluation Protocol for Fine-grained Predictive Testing",
Author= "David Brodmann, Erik Rodner",
Doi= "https://doi.org/10.30844/AKWI_2022_19",
Abstract= "Systematic testing of every single component and interface is undoubtedly an important
measure to handle the complex nature of current software systems. However, this comes with often
neglected computational costs. The aim of this paper is therefore to cut time and resource needs by
predictive testing, i.e., predicting test failures with machine learning using a surprisingly simple
statistical feature representation. Furthermore, we present the first open research benchmark for pre-
dictive testing to enable and foster future research in this area",
Keywords= "machine learning; software testing; research dataset; predictive testing",
}
David Brodmann, Erik Rodner(2022): OpenPredict - An Open Research Dataset and Evaluation Protocol for Fine-grained Predictive Testing. AKWI-Tagungsband zur 35. AKWI-Jahrestagung(2022), S. S. 287–303. Online: https://doi.org/10.30844/AKWI_2022_19 (Abgerufen 26.04.24)
Open Access
Systematic testing of every single component and interface is undoubtedly an important measure to handle the complex nature of current software systems. However, this comes with often neglected computational costs. The aim of this paper is therefore to cut time and resource needs by predictive testing, i.e., predicting test failures with machine learning using a surprisingly simple statistical feature representation. Furthermore, we present the first open research benchmark for pre- dictive testing to enable and foster future research in this area
machine learning; software testing; research dataset; predictive testing