We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. We train and evaluate our model using a dataset that we collected from commit histories of open-source software projects, with each example consisting of a concurrent update to a method and its corresponding comment. We compare our approach against multiple baselines using both automatic metrics and human evaluation. Results reflect the challenge of this task and that our model outperforms baselines with respect to making edits.

Haplotype phasing represents an essential step in studying the association of genomic polymorphisms with complex genetic diseases, and in determining targets for drug designing. In recent years, huge amounts of genotype data are produced from the rapidly evolving high-throughput sequencing technologies, and the data volume challenges the community with more efficient haplotype phasing algorithms, in the senses of both running time and overall accuracy. 2SNP is one of the fastest haplotype phasing algorithms with comparable low error rates with the other algorithms. The most time-consuming step of 2SNP is the construction of a maximum spanning tree (MST) among all the heterozygous SNP pairs. We simplified this step by replacing the MST with the initial haplotypes of adjacent heterozygous SNP pairs. The multi-SNP haplotypes were estimated within a sliding window along the chromosomes. The comparative studies on four different-scale genotype datasets suggest that our algorithm WinHAP outperforms 2SNP and most of the other haplotype phasing algorithms in terms of both running speeds and overall accuracies. To facilitate the WinHAP’s application in more practical biological datasets, we released the software for free at: http://staff.ustc.edu.cn/~xuyun/winhap/index.htm.

Coding conventions for naming, spacing, and other essentially stylistic properties are necessary for developers to effectively understand, review, and modify source code in large software projects. Consistent conventions in verification projects based on proof assistants, such as Coq, increase in importance as projects grow in size and scope. While conventions can be documented and enforced manually at high cost, emerging approaches automatically learn and suggest idiomatic names in Java-like languages by applying statistical language models on large code corpora. However, due to its powerful language extension facilities and fusion of type checking and computation, Coq is a challenging target for automated learning techniques. We present novel generation models for learning and suggesting lemma names for Coq projects. Our models, based on multi-input neural networks, are the first to leverage syntactic and semantic information from Coq's lexer (tokens in lemma statements), parser (syntax trees), and kernel (elaborated terms) for naming; the key insight is that learning from elaborated terms can substantially boost model performance. We implemented our models in a toolchain, dubbed Roosterize, and applied it on a large corpus of code derived from the Mathematical Components family of projects, known for its stringent coding conventions. Our results show that Roosterize substantially outperforms baselines for suggesting lemma names, highlighting the importance of using multi-input models and elaborated terms.

We present the design and implementation of GVM, the first system for executing Java bytecode entirely on GPUs. GVM is ideal for applications that execute a large number of short-living tasks, which share a significant fraction of their codebase and have similar execution time. GVM uses novel algorithms, scheduling, and data layout techniques to adapt to the massively parallel programming and execution model of GPUs. We apply GVM to generate and execute tests for Java projects. First, we implement a sequence-based test generation on top of GVM and design novel algorithms to avoid redundant test sequences. Second, we use GVM to execute randomly generated test cases. We evaluate GVM by comparing it with two existing Java bytecode interpreters (Oracle JVM and Java Pathfinder), as well as with the Oracle JVM with just-in-time (JIT) compiler, which has been engineered and optimized for over twenty years. Our evaluation shows that sequence-based test generation on GVM outperforms both Java Pathfinder and Oracle JVM interpreter. Additionally, our results show that GVM performs as well as running our parallel sequence-based test generation algorithm using JVM with JIT with many CPU threads. Furthermore, our evaluation on several classes from open-source projects shows that executing randomly generated tests on GVM outperforms sequential execution on JVM interpreter and JVM with JIT. CCS Concepts: • Software and its engineering → Object oriented languages; Interpreters; Runtime environments; Software testing and debugging.

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