Pereira Borges Jr., NatanielHavrikov, NikolasZeller, AndreasKoziolek, AnneSchaefer, InaSeidl, Christoph2020-12-172020-12-172021978-3-88579-704-3https://dl.gi.de/handle/20.500.12116/34527To systematically test a program, one needs good inputs—inputs that are valid such that they are not rejected by the program, and inputs that cover as much of the input space as possible in order to reach a maximum of functionality. We present recent techniques to systematically cover input structure. Our k-path algorithm for grammar production [HZ19] systematically covers syntactic elements of the input as well as their combinations. We show how to learn such input structures from graphical user interfaces, notably their interaction language [DBZ19]. Finally, we demonstrate that knowledge bases such as DBPedia can be a reliable source of semantically coherent inputs [Wa20]. All these techniques result in a significantly higher code coverage than state of the art.engrammarcoverageautomated testinginput generationknowledge-baseandroidText/ConferencePaper10.18420/SE2021_311617-5468