Biblio

Deep Learning methods have not only shown to improve software performance in compiler heuristics, but also e.g. to improve security in vulnerability prediction or to boost developer productivity in software engineering tools. A key to the success of such methods across these use cases is the expressiveness of the representation used to abstract from the program code. Recent work has shown that different such representations have unique advantages in terms of performance. However, determining the best-performing one for a given task is often not obvious and requires empirical evaluation. Therefore, we present ComPy-Learn, a toolbox for conveniently defining, extracting, and exploring representations of program code. With syntax-level language information from the Clang compiler frontend and low-level information from the LLVM compiler backend, the tool supports the construction of linear and graph representations and enables an efficient search for the best-performing representation and model for tasks on program code.

The article considers the approach to identifying potentially unsafe data in program code of embedded systems which can lead to errors and fails in the functioning of equipment. The sources of invalid data are revealed and the process of changing the status of this data in process of static code analysis is shown. The mechanism for annotating functions that operate on unsafe data is described, which allows to control the entire process of using them and thus it will improve the quality of the output code.