Search

This paper focuses on Code Generation task that aims at generating relevant code fragments according to given natural language descriptions. In the process of software development, developers often encounter two scenarios. One is requested to write a large amount of repetitive and low-technical code for implementing common functionalities. The other is writing code that depends on specific task requirements, which may necessitate the use of external resources such as documentation or other tools. Therefore, code generation has received a lot of attention among academia and industry for assisting developers in coding. In fact, it has also been one of the key concerns in the field of software engineering to make machines understand users' requirements and write programs on their own. The recent development of deep learning techniques especially pre-training models make the code generation task achieve promising performance. In this paper, we systematically review the current work on deep learning-based code generation and classify the current deep learning-based code generation methods into three categories: methods based on code features, methods incorporated with retrieval, and methods incorporated with post-processing. The first category refers to the methods that use deep learning algorithms for code generation based on code features, and the second and third categories of methods improve the performance of the methods in the first category. In this paper, the existing research results of each category of methods are systematically reviewed, summarized and commented. Besides, the paper summarizes and analyzes the corpus and the popular evaluation metrics used in the existing code generation work. Finally, the paper summarizes the overall literature review and provides a prospect on future research directions worthy of attention., Comment: in Chinese language

Software plays a crucial role in our daily lives, and therefore the quality and security of software systems have become increasingly important. However, vulnerabilities in software still pose a significant threat, as they can have serious consequences. Recent advances in automated program repair have sought to automatically detect and fix bugs using data-driven techniques. Sophisticated deep learning methods have been applied to this area and have achieved promising results. However, existing benchmarks for training and evaluating these techniques remain limited, as they tend to focus on a single programming language and have relatively small datasets. Moreover, many benchmarks tend to be outdated and lack diversity, focusing on a specific codebase. Worse still, the quality of bug explanations in existing datasets is low, as they typically use imprecise and uninformative commit messages as explanations. To address these issues, we propose an automated collecting framework REEF to collect REal-world vulnErabilities and Fixes from open-source repositories. We focus on vulnerabilities since they are exploitable and have serious consequences. We develop a multi-language crawler to collect vulnerabilities and their fixes, and design metrics to filter for high-quality vulnerability-fix pairs. Furthermore, we propose a neural language model-based approach to generate high-quality vulnerability explanations, which is key to producing informative fix messages. Through extensive experiments, we demonstrate that our approach can collect high-quality vulnerability-fix pairs and generate strong explanations. The dataset we collect contains 4,466 CVEs with 30,987 patches (including 236 CWE) across 7 programming languages with detailed related information, which is superior to existing benchmarks in scale, coverage, and quality. Evaluations by human experts further confirm that our framework produces high-quality vulnerability explanations. © 2023 IEEE.

Exponential increase of data has motivated advances of data storage technologies. As a promising storage media, DeoxyriboNucleic Acid (DNA) storage provides a much higher data density and superior durability, compared with state-of-the-art media. In this paper, we provide a tutorial on DNA storage and its role in molecular communications. Firstly, we introduce fundamentals of DNA-based molecular communications and storage (MCS), discussing the basic process of performing DNA storage in MCS. Furthermore, we provide tutorials on how conventional coding schemes that are used in wireless communications can be applied to DNA-based MCS, along with numerical results. Finally, promising research directions on DNA-based data storage in molecular communications are introduced and discussed in this paper.

Software plays a crucial role in our daily lives, and therefore the quality and security of software systems have become increasingly important. However, vulnerabilities in software still pose a significant threat, as they can have serious consequences. Recent advances in automated program repair have sought to automatically detect and fix bugs using data-driven techniques. Sophisticated deep learning methods have been applied to this area and have achieved promising results. However, existing benchmarks for training and evaluating these techniques remain limited, as they tend to focus on a single programming language and have relatively small datasets. Moreover, many benchmarks tend to be outdated and lack diversity, focusing on a specific codebase. Worse still, the quality of bug explanations in existing datasets is low, as they typically use imprecise and uninformative commit messages as explanations. To address these issues, we propose an automated collecting framework REEF to collect REal-world vulnErabilities and Fixes from open-source repositories. We develop a multi-language crawler to collect vulnerabilities and their fixes, and design metrics to filter for high-quality vulnerability-fix pairs. Furthermore, we propose a neural language model-based approach to generate high-quality vulnerability explanations, which is key to producing informative fix messages. Through extensive experiments, we demonstrate that our approach can collect high-quality vulnerability-fix pairs and generate strong explanations. The dataset we collect contains 4,466 CVEs with 30,987 patches (including 236 CWE) across 7 programming languages with detailed related information, which is superior to existing benchmarks in scale, coverage, and quality. Evaluations by human experts further confirm that our framework produces high-quality vulnerability explanations., Comment: Accepted by ASE 2023 Industry Challenge(Competition) Track

This work was supported by the Degree and Graduate Education Reform Key Projects of Guangdong Province, and the title is Exploration and Practice of Postgraduate Cultivation in Incorporating Innovation and Entrepreneurship Consciousness. Also, it was supported by the project of China Higher Education Association Thirteenth Five-Year Plan for Scientific Research in Higher Education (Grant number: 2018SYSZD08). Abstract Under the background of the innovation and entrepreneurship ability improvement plan proposed by the Ministry of education of China, the research on the subject of entrepreneurship and innovation is conducive to the continuous deepening of innovation and entrepreneurship education reform in Colleges and universities, and the continuous training and delivery of high-level talents for the construction of an innovative country. This paper explores the relationship among innovation and entrepreneurship ability, innovation and entrepreneurship education psychological quality and innovation and entrepreneurship willingness through a questionnaire survey of graduate students in Colleges and universities, analyzes the current situation, further confirms the hypothesis among the three, obtains positive results, and puts forward some countermeasures and suggestions for higher education. Keywords: Higher education, Graduate students, Willingness of innovation and entrepreneurship, Innovation and entrepreneurship ability, Psychological quality of innovation and entrepreneurship education DOI: 10.7176/JEP/11-19-06 Publication date:July 31st 2020

This work was supported by the Degree and Graduate Education Reform Key Projects of Guangdong Province, and the title is Exploration and Practice of Postgraduate Cultivation in Incorporating Innovation and Entrepreneurship Consciousness. Also, it was supported by the project of China Higher Education Association Thirteenth Five-Year Plan for Scientific Research in Higher Education (Grant number: 2018SYSZD08). Abstract Under the background of the innovation and entrepreneurship ability improvement plan proposed by the Ministry of education of China, the research on the subject of entrepreneurship and innovation is conducive to the continuous deepening of innovation and entrepreneurship education reform in Colleges and universities, and the continuous training and delivery of high-level talents for the construction of an innovative country. This paper explores the relationship among innovation and entrepreneurship ability, innovation and entrepreneurship education psychological quality and innovation and entrepreneurship willingness through a questionnaire survey of graduate students in Colleges and universities, analyzes the current situation, further confirms the hypothesis among the three, obtains positive results, and puts forward some countermeasures and suggestions for higher education. Keywords: Higher education, Graduate students, Willingness of innovation and entrepreneurship, Innovation and entrepreneurship ability, Psychological quality of innovation and entrepreneurship education DOI: 10.7176/JEP/11-19-06 Publication date:July 31st 2020