Among possible future developments of the research, is the definition of new training sets to validate the results and an expansion of the test set. Both these developments, however, will require an extensive digitization effort: most of the useful texts, in fact, are not available in a clean plain-text format. In addition, other software should be tested on the already defined corpus, from JGAAP (see Juola <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">et al. </hi>2008) to the CLEF/PAN software (see Stamatatos <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">et al.</hi> 2014), focusing specifically on different methods for authorship attribution, from lower-level features such as character n-grams (see Halvani <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">et al. </hi>2016), to higher-level features such as syntactic labels (see Hirst / Feiguina 2007), taking into consideration also machine-learning techniques (see Jockers / Witten 2010). With our study, we hope to have cast the groundwork for a research that can have long-lasting consequences on the history of German literature, confirming at the same time how quantitative methods are not in opposition, but complementary to qualitative analysis (see Herrmann 2018).