An automation engineering system (100; 700) comprising: plural modules comprising a feature selection module and a learning module which includes one or more neural networks; a processor for executing the plural modules; and a memory for storing the plural modules; wherein the automation engineering system (100; 700) is configured to train the one or more neural networks of the learning module based on automation source code including a first data set of automation source code being programmable logic controller source code for programmable logic controllers (PLCs) and a second data set of automation source code being automation source code for manufacturing systems; wherein the feature selection module is configured to select features that are extracted from the automation source code (114, 116) of the first and second data sets (110, 112), and to provide one or more selected features from the automation source code (114, 116) of the first and second data sets (110, 112) in one or more combinations to the learning module for tuning the one or more neural networks of the learning module based on the one or more selected features or one or more combinations of the selected features; wherein the learning module is configured, based on the training based on the automation source code (114, 116) of the first and second data sets (110, 112), to generate code embeddings that represent snippets (500, 502) of automation source code as respective vectors in a vector space by mapping code to vectors in the vector space such that the automation source code (114, 116) of the first and second data sets (110, 112) is associated with multiple vectors in the vector space; and wherein, for generating the code embeddings, the learning module includes a first processor configured to perform a document to vector (doc2vec) algorithm and a second processor configured to perform a term frequency-inverse document frequency (tf-idf) algorithm. plural modules comprising a feature selection module and a learning module which includes one or more neural networks; a processor for executing the plural modules; and a memory for storing the plural modules; wherein the automation engineering system (100; 700) is configured to train the one or more neural networks of the learning module based on automation source code including a first data set of automation source code being programmable logic controller source code for programmable logic controllers (PLCs) and a second data set of automation source code being automation source code for manufacturing systems; wherein the feature selection module is configured to select features that are extracted from the automation source code (114, 116) of the first and second data sets (110, 112), and to provide one or more selected features from the automation source code (114, 116) of the first and second data sets (110, 112) in one or more combinations to the learning module for tuning the one or more neural networks of the learning module based on the one or more selected features or one or more combinations of the selected features; wherein the learning module is configured, based on the training based on the automation source code (114, 116) of the first and second data sets (110, 112), to generate code embeddings that represent snippets (500, 502) of automation source code as respective vectors in a vector space by mapping code to vectors in the vector space such that the automation source code (114, 116) of the first and second data sets (110, 112) is associated with multiple vectors in the vector space; and wherein, for generating the code embeddings, the learning module includes a first processor configured to perform a document to vector (doc2vec) algorithm and a second processor configured to perform a term frequency-inverse document frequency (tf-idf) algorithm.