Add some papers.

Author: mallamanis

_publications/li2022codereviewer.markdown

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+---
+layout: publication
+title: "CodeReviewer: Pre-Training for Automating Code Review Activities"
+authors: Zhiyu Li, Shuai Lu, Daya Guo, Nan Duan, Shailesh Jannu, Grant Jenks, Deep Majumder, Jared Green, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan
+conference: 
+year: 2022
+bibkey: li2022codereviewer
+additional_links:
+   - {name: "ArXiV", url: "https://arxiv.org/abs/2203.09095"}
+tags: ["review"]
+---
+Code review is an essential part to software development lifecycle since it aims at guaranteeing the quality of codes. Modern code review activities necessitate developers viewing, understanding and even running the programs to assess logic, functionality, latency, style and other factors. It turns out that developers have to spend far too much time reviewing the code of their peers. Accordingly, it is in significant demand to automate the code review process. In this research, we focus on utilizing pre-training techniques for the tasks in the code review scenario. We collect a large-scale dataset of real world code changes and code reviews from open-source projects in nine of the most popular programming languages. To better understand code diffs and reviews, we propose CodeReviewer, a pre-trained model that utilizes four pre-training tasks tailored specifically for the code review senario. To evaluate our model, we focus on three key tasks related to code review activities, including code change quality estimation, review comment generation and code refinement. Furthermore, we establish a high-quality benchmark dataset based on our collected data for these three tasks and conduct comprehensive experiments on it. The experimental results demonstrate that our model outperforms the previous state-of-the-art pre-training approaches in all tasks. Further analysis show that our proposed pre-training tasks and the multilingual pre-training dataset benefit the model on the understanding of code changes and reviews. 

_publications/siow2022learning.markdown

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+---
+layout: publication
+title: "Learning Program Semantics with Code Representations: An Empirical Study"
+authors: Jing Kai Siow, Shangqing Liu, Xiaofei Xie, Guozhu Meng, Yang Liu
+conference: SANER
+year: 2022
+bibkey: siow2022learning
+additional_links:
+   - {name: "ArXiV", url: "https://arxiv.org/abs/2203.11790"}
+tags: ["representation"]
+---
+Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works propose diverse approaches to learn the program semantics for different tasks and these works have achieved state-of-the-art performance. However, currently, a comprehensive and systematic study on evaluating different program representation techniques across diverse tasks is still missed.
+
+From this starting point, in this paper, we conduct an empirical study to evaluate different program representation techniques. Specifically, we categorize current mainstream code representation techniques into four categories i.e., Feature-based, Sequence-based, Tree-based, and Graph-based program representation technique and evaluate its performance on three diverse and popular code intelligent tasks i.e., {Code Classification}, Vulnerability Detection, and Clone Detection on the public released benchmark. We further design three {research questions (RQs)} and conduct a comprehensive analysis to investigate the performance. By the extensive experimental results, we conclude that (1) The graph-based representation is superior to the other selected techniques across these tasks. (2) Compared with the node type information used in tree-based and graph-based representations, the node textual information is more critical to learning the program semantics. (3) Different tasks require the task-specific semantics to achieve their highest performance, however combining various program semantics from different dimensions such as control dependency, data dependency can still produce promising results.