In collaborative software development, program merging isthemechanism to integrate changes from multiple programmers. Merge algorithms in modern version control systems report a conflict when changes interfere textually. Merge conflicts require manual intervention and frequently stall modern continuous integration pipelines. Prior work found that, although costly, a large majority of resolutions involve re-arranging text without writing any new code. Inspired by this observation we propose thefirst data-driven approachto resolve merge conflicts with a machine learning model. We realize our approach in a toolDeepMergethat uses a novel combination of (i) an edit-aware embedding of merge inputs and (ii) a variation of pointer networks, to construct resolutions from input segments. We also propose an algorithm to localize manual resolutions in a resolved file and employ it to curate a ground-truth dataset comprising 8,719 non-trivial resolutions in JavaScript programs. Our evaluation shows that, on a held out test set,DeepMergecan predict correct resolutions for 37% of non-trivial merges, compared to only 4% by a state-of-the-art semistructured merge technique. Furthermore, on the subset of merges with upto 3 lines (comprising 24% of the total dataset),DeepMergecan predict correct resolutions with 78% accuracy.

Testing is widely recognized as an important stage of the software development lifecycle. Effective software testing can provide benefits such as bug finding, preventing regressions, and documentation. In terms of documentation, unit tests express a unit's intended functionality, as conceived by the developer. A test oracle, typically expressed as an condition, documents the intended behavior of a unit under a given test prefix. Synthesizing a functional test oracle is a challenging problem, as it must capture the intended functionality rather than the implemented functionality.

Collaborative software development is an integral part of the modern software development life cycle, essential to the success of large-scale software projects. When multiple developers make concurrent changes around the same lines of code, a merge conflict may occur. Such conflicts stall pull requests and continuous integration pipelines for hours to several days, seriously hurting developer productivity. To address this problem, we introduce MergeBERT, a novel neural program merge framework based on token-level three-way differencing and a transformer encoder model. By exploiting the restricted nature of merge conflict resolutions, we reformulate the task of generating the resolution sequence as a classification task over a set of primitive merge patterns extracted from real-world merge commit data. Our model achieves 63–68% accuracy for merge resolution synthesis, yielding nearly a 3× performance improvement over existing semi-structured, and 2× improvement over neural program merge tools. Finally, we demonstrate that MergeBERT is sufficiently flexible to work with source code files in Java, JavaScript, TypeScript, and C# programming languages. To measure the practical use of MergeBERT, we conduct a user study to evaluate MergeBERT suggestions with 25 developers from large OSS projects on 122 real-world conflicts they encountered. Results suggest that in practice, MergeBERT resolutions would be accepted at a higher rate than estimated by automatic metrics for precision and accuracy. Additionally, we use participant feedback to identify future avenues for improvement of MergeBERT.

With surging enrollment in Computer Science courses at both the introductory and advanced level, it is critical to leverage automated testing and grading to ensure consistent assessment of student learning. Program analysis tools allow us to streamline the grading process so instructors and TAs can spend more time teaching, one-on-one tutoring, and mentoring students. We present complex use cases of automated assignment testing and grading within the open-source homework submission system, Submitty. Students receive immediate and detailed feedback from the automated grader, and can resubmit to correct errors. Submitty uses custom-built grading tools, including difference checking of plaintext program output, instructor authored assignment-specific custom graders, and static analysis tools that reason about program structure. In addition, it employs a variety of external tools, including version control (Git and SVN), unit testing frameworks (JUnit), memory debugging tools (Valgrind and DrMemory), and code coverage tools (Emma). In this poster we describe our experience with memory debugging and code coverage tools, and outline plans to include immutability inference and verification.