Sequence to Sequence Machine Learning for Automatic Program Repair

Abstract: Most of previous program repair approaches are only able to generate fixes for one-line bugs, including machine learning based approaches. This work aims to reveal whether such a system with the state of the art technique is able to make useful predictions while being fed by whole source files. To verify whether multi-line bugs can be fixed using a state of the art solution a system has been created, using already existing Neural Machine Translation tools and data gathered from GitHub. The result of the finished system shows however, that the method used in this thesis is not sufficient to get satisfying results. No bug has successfully been corrected by the system. Although the results are poor there are still unexplored approaches to the project that possibly could improve the performance of the system. One way being narrowing down the input data to method level of source code instead of file level.