Jing Kai Siow

Jing Kai Siow, Shangqing Liu, Xiaofei Xie et al.

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.

Yaqin Zhou, Jing Kai Siow, Chenyu Wang et al.

Security patches in open-source software, providing security fixes to identified vulnerabilities, are crucial in protecting against cyberattacks. Despite the National Vulnerability Database (NVD) publishes identified vulnerabilities, a vast majority of vulnerabilities and their corresponding security patches remain beyond public exposure, e.g., in the open-source libraries that are heavily relied on by developers. An extensive security patches dataset could help end-users such as security companies, e.g., building a security knowledge base, or researchers, e.g., aiding in vulnerability research. To curate security patches including undisclosed patches at a large scale and low cost, we propose a deep neural-network-based approach built upon commits of open-source repositories. We build security patch datasets that include 38,291 security-related commits and 1,045 CVE patches from four C libraries. We manually verify each commit, among the 38,291 security-related commits, to determine if they are security-related. We devise a deep learning-based security patch identification system that consists of two neural networks: one commit-message neural network that utilizes pretrained word representations learned from our commits dataset; and one code-revision neural network that takes code before and after revision and learns the distinction on the statement level. Our evaluation results show that our system outperforms SVM and K-fold stacking algorithm, achieving as high as 87.93% F1-score and precision of 86.24%. We deployed our pipeline and learned model in an industrial production environment to evaluate the generalization ability of our approach. The industrial dataset consists of 298,917 commits from 410 new libraries that range from a wide functionality. Our experiment results and observation proved that our approach identifies security patches effectively among open-sourced projects.

Shangqing Liu, Yu Chen, Xiaofei Xie et al.

Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Most previous approaches either rely on retrieval-based (which can take advantage of similar examples seen from the retrieval database, but have low generalization performance) or generation-based methods (which have better generalization performance, but cannot take advantage of similar examples). This paper proposes a novel retrieval-augmented mechanism to combine the benefits of both worlds. Furthermore, to mitigate the limitation of Graph Neural Networks (GNNs) on capturing global graph structure information of source code, we propose a novel attention-based dynamic graph to complement the static graph representation of the source code, and design a hybrid message passing GNN for capturing both the local and global structural information. To evaluate the proposed approach, we release a new challenging benchmark, crawled from diversified large-scale open-source C projects (total 95k+ unique functions in the dataset). Our method achieves the state-of-the-art performance, improving existing methods by 1.42, 2.44 and 1.29 in terms of BLEU-4, ROUGE-L and METEOR.

Guozhu Meng, Yinxing Xue, Jing Kai Siow et al.

Data driven research on Android has gained a great momentum these years. The abundance of data facilitates knowledge learning, however, also increases the difficulty of data preprocessing. Therefore, it is non-trivial to prepare a demanding and accurate set of data for research. In this work, we put forward AndroVault, a framework for the Android research composing of data collection, knowledge representation and knowledge extraction. It has started with a long-running web crawler for data collection (both apps and description) since 2013, which guarantees the timeliness of data; With static analysis and dynamic analysis of the collected data, we compute a variety of attributes to characterize Android apps. After that, we employ a knowledge graph to connect all these apps by computing their correlation in terms of attributes; Last, we leverage multiple technologies such as logical inference, machine learning, and correlation analysis to extract facts (more accurate and demanding, either high level or not, data) that are beneficial for a specific research problem. With the produced data of high quality, we have successfully conducted many research works including malware detection, code generation, and Android testing. We would like to release our data to the research community in an authenticated manner, and encourage them to conduct productive research.