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学术报告(2024.11.6)

发布日期:2024-11-01      新闻来源:       责任编辑:5127      地点:计算机A501                日期:2024.11.6

报告一

报告题目: 基于程序综合的Java虚拟机测试

报告人:  陈俊洁

报告时间:2024116 14:30-15:30

报告地点:计算机学院A501

报告摘要: 

        Java虚拟机是重要的基础软件,其测试输入为相应程序语言下编写的程序。本研究提出基于程序综合的Java虚拟机测试技术,利用历史缺陷数据、代码表征技术,以及数据流分析方法,实现测试程序的有效构建,提升揭错效果。成果已为开源Java虚拟机HotSpotOpenJ9检测到数十个真实缺陷,并落地工业界。

报告人简介:

   陈俊洁,天津大学智能与计算学部教授,软件工程团队负责人,博士生导师,国家优青项目获得者,博士毕业于北京大学;研究方向主要为基础软件测试、可信人工智能、数据驱动的软件工程等;入选中国科协青年人才托举工程、斯坦福大学发布的全球前2%顶尖科学家年度榜单,荣获CCF优博、电子学会自然科学一等奖、七项最佳/杰出论文奖等奖项;成果在华为、百度等多家知名企业落地;担任CCF系统软件专委常委,CCF 218club副主席,CCF 118club执行委员,以及CCF-A类会议ASE评审过程主席,ICSEFSEASEISSTA等顶会PC





报告二


报告题目:How AI is Changing the World of Software Engineering

报告人:  张涛
报告时间:
2024116 15:30-16:30

报告地点:计算机学院A501

报告摘要:

          With the rapid growth of AI, it deeply influences almost all of computer science areas, especially for software engineering. Software development process generates a large amount of corpus data (such as defect reports, source code, logs, etc.). How to use these corpus data to better implement automated software engineering tasks is a big challenge. The difficulty lies in the semantic gap between natural language and programming language. In this new era, generative AI can help automatically produce more reliable source code, patches, commits, code comments, and responses to the user reviews by deeply analyzing the semantic relations between natural language and programming language. For achieving the best performance of automated software engineering tasks, a lot of software engineering scholars walk through a long road. For our team, we started from the initial reliance on bug reports or user review information to perform a single automated software engineering task. By establishing a unified neural network model and a unified representation model for bug reports, we constructed a set of methods that can achieve multiple automated software engineering tasks. In the process, we discovered the over-interpretation problem of pre-trained language models when implementing automated software engineering tasks, and proposed mitigation strategies. Following this way, depended on the huge power of LLMs, we proposed a series of new models and corresponding tools to enhance the performance of automated software engineering tasks.

报告人简介:

   张涛,博士,澳门科技大学计算机科学与工程学院副教授、ACM/IEEE/CCF高级会员,粤港澳高校区块链联盟秘书长。主要研究方向包括智能化软件工程、软件安全、智能合约漏洞检测等。发表论文90余篇,论文谷歌学术引用超过2300次,H指数为28。主持国家自然科学基金、澳门科学技术发展基金等多个项目。担任软件工程领域知名国际会议SANER 2023的大会主席和Internetware 2024的程序委员会主席。担任软件工程领域权威国际期刊TSEJSS的副编辑。多次担任软件工程领域三大顶级会议(即ICSE, FSE, ASE)的程序委员会委员。