@Inbook{Brodmann+Rodner,
							Cite-key = "brodmann2022", 
							Year= "2022", 
							 
							 Volume= "AKWI-Tagungsband zur 35. AKWI-Jahrestagung", 
							Pages= "S. 287–303", 
							Journal   = "Monographien",
							 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", 
							}