Utilizing Source Code Embeddings to Identify Correct Patches

The so called Generate-and-Validate approach of Automatic Program Repair consists of two main activities, the generate activity, which produces candidate solutions to the problem, and the validate activity, which checks the correctness of the generated solutions. The latter however might not give a reliable result, since most of the techniques establish the correctness of the solutions by (re-)running the available test cases. A program is marked as a possible fix, if it passes all the available test cases. Although tests can be run automatically, in real life applications the problem of over- and underfitting often occurs, resulting in inadequate patches. At this point manual investigation of repair candidates is needed although they passed the tests. Our goal is to investigate ways to predict correct patches. The core idea is to exploit textual and structural similarity between the original (buggy) program and the generated patches. To do so we apply Doc2vec and Bert embedding methods on source code. So far APR tools generate mostly one-line fixes, leaving most of the original source code intact. Our observation was, that patches which bring in new variables, make larger changes in the code are usually the incorrect ones. The proposed approach was evaluated on the QuixBugs dataset consisting of 40 bugs and fixes belonging to them. Our approach successfully filtered out 45% of the incorrect patches.