Automatic generation of software bug fixes based on analysis of software repositories

This paper describes the method, which is developed by the authors to automated correction of software errors, which is based on the analysis of successful project fixes for the ABAP programming language available in open repositories. The method generates the candidates of patches based on predefined templates and ranks the results by the probability of successful application, which is determined by a probabilistic model using machine learning methods. The probabilistic model is formed by training on features, which are extracted from data from successful and unsuccessful patches of ABAP programs in open repositories. The developed method is tested on synthetic examples and real projects with errors in the ABAP language. As a result of the experiments, the method successfully generated some patches, which showed their efficiency. The results in accuracy and efficiency are comparable or superior to the results of experiments in similar works by other authors.