Commit messages concisely describe code changes in natural language and are important for program comprehension and maintenance. Previous studies proposed some approaches for automatic commit message generation, but their performance is limited due to inappropriate representation of code changes and improper combination of translation-based and retrieval-based approaches. To address these problems, this paper introduces a novel framework named COME, in which modification embeddings are used to represent code changes in a fine-grained way, a self-supervised generative task is designed to learn contextualized code change representation, and retrieval-based and translation-based methods are combined through a decision algorithm. The average improvement of COME over the state-of-the-art approaches is 9.2% on automatic evaluation metrics and 8.0% on human evaluation metrics. We also analyse the effectiveness of COME's three main components and each of them results in an improvement of 8.6%, 8.7% and 5.2%.

Commit message generation (CMG) is a challenging task in automated software engineering that aims to generate natural language descriptions of code changes for commits. Previous methods all start from the modified code snippets, outputting commit messages through template-based, retrieval-based, or learning-based models. While these methods can summarize what is modified from the perspective of code, they struggle to provide reasons for the commit. The correlation between commits and issues that could be a critical factor for generating rational commit messages is still unexplored. In this work, we delve into the correlation between commits and issues from the perspective of dataset and methodology. We construct the first dataset anchored on combining correlated commits and issues. The dataset consists of an unlabeled commit-issue parallel part and a labeled part in which each example is provided with human-annotated rational information in the issue. Furthermore, we propose ExGroFi (Extraction, Grounding, Ene-tuning), a novel paradigm that can introduce the correlation between commits and issues into the training phase of models. To evaluate whether it is effective, we perform comprehensive experiments with various state-of-the-art CMG models. The results show that compared with the original models, the performance of ExGroFi-enhanced models is significantly improved.

In this paper, we explore methods to generate optimal network topologies for wireless sensor networks (WSNs) with and without obstacles. Specifically, we investigate a dense network with n sensor nodes and m=nb(0v) (0 <; v ≤ 1) arbitrarily or randomly distributed obstacles, which block cells they are located in, i.e., sensor nodes cannot be placed in these cells and nodes' communication cannot cross them directly. We find that the overall throughput capacity is bounded by the transmission burden in areas around these blocked cells and introduce a novel algorithm of complexity O(M) to generate optimal sensor nodes' topologies for any given obstacles' distributions. We further analyze its performance for regularly distributed obstacles, which can be taken to estimate the lower bound of the algorithm's performance.