报告题目：Automated Synthesis of Semantic Malware Signatures using Maximum Satisfiability (NDSS'17)

报告人：Yu Feng

报告人简介:

Yu Feng is a final year Ph.D. candidate at UT Austin advised by Isil Dillig and he was a member of the STAMP group led by Alex Aiken. Yu has interests spanning programming languages, security, and software engineering. For instance, he has published topics about Android Malware Detection (fse'14, ndss'17), Program Synthesis (popl'17, pldi'17) and Program Analysis (oopsla'15, aplas'15).

摘要：

In response to the rapid dissemination of Android malware, there is a real need for tools that can automatically detect malicious applications that steal private user information. In this talk, I will discuss how to automatically perform semantics-based malware detection through static analysis. Specifically, I will present a technique for automatically synthesizing malware signatures from very few samples of a malware family. The key idea underlying our technique is to look for a maximally suspicious common subgraph (MSCS) that is shared between all known instances of a malware family.

关键字：synthesis, malware signature, MaxSAT

报告题目：A Comprehensive Study on Real World Concurrency Bugs in Node.js (ASE'17)

报告人：王杰

报告人简介:

Jie Wang, a 3rd year Phd student from Institute of Software, Chinese Academy of Sciences (ISCAS). Her advisors are Jun Wei and Wensheng Dou. Her research interests focus on program analysis, especially testing and debugging javascript applications. She has now published 4 first author papers, in issre'15, jss, fse'16 - SRC, and ase'17 respectively.

摘要：

Node.js becomes increasingly popular in building server-side JavaScript applications. Node.js adopts an event-driven model, which supports asynchronous I/O and non-deterministic event processing. This asynchrony and non-determinism can introduce intricate concurrency bugs, and leads to unpredictable behaviors. An in-depth understanding of real world concurrency bugs in Node.js applications will significantly promote effective techniques in bug detection, testing and fixing for Node.js.
In this paper, we present a comprehensive study on real world concurrency bugs in Node.js applications. Specifically, we have carefully studied 57 real bug cases from open-source Node.js applications, and analyzed their bug characteristics, e.g., bug patterns and root causes, bug impacts, bug manifestation, and fix strategies. Through this study, we obtain several interesting findings, which may open up many new research directions in combating concurrency bugs in Node.js. For example, one finding is that two thirds of the bugs are caused by atomicity violation. However, due to lack of locks and transaction mechanism, Node.js cannot easily express and guarantee atomic intention.

关键字：JavaScript, Node.js, event-driven, concurrency bug, empirical study

报告题目：系统集成测试中测试预警原因自动分析技术 (What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing, ICSE'17)

报告人：李晓晨

报告人简介:

李晓晨，大连理工大学软件学院，博士生。导师，江贺教授。他2008年毕业于哈尔滨市第三中学，后考入大连理工大学，攻读学士、硕士、博士学位。他的研究方向是软件仓库挖掘，日志分析。他于2009年-2014年获得大连理工大学本科/硕士学习一等/二等奖学金，2012年获得大连理工大学优秀毕业生。目前在国际会议ICSE'17上发表论文一篇（二作），SANER'16和SSBSE'15发表论文各一篇（三作）。另外，他曾担任HASQ'16会议的审稿人。

摘要：

Driven by new software development process and testing on clouds, system and integration testing nowadays tends to produce enormous number of alarms. Such test alarms lay an almost unbearable burden on software testing engineers who have to manually analyze the causes of these alarms. The causes are critical because they decide which stakeholders are responsible to fix the bugs detected during the testing. In this paper, we present a novel approach that aims to relieve the burden by automating the procedure. Our approach, called Cause Analysis Model, exploits information retrieval techniques to efficiently infer test alarm causes based on test logs. We have developed a prototype and evaluated our tool on two industrial datasets with more than 14,000 test alarms. Experiments on the two datasets show that our tool achieves an accuracy of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per cause analysis. Due to the attractive experimental results, our industrial partner, a leading information and communication technology company in the world, has deployed the tool and it achieves an average accuracy of 72% after two months’ running, nearly three times more accurate than a previous strategy based on regular expressions.

关键字：software testing, system and integration testing, test alarm analysis, multiclass classification

报告题目：一种无监督的API解释教程片段推荐方法 (An Unsupervised Approach for Discovering Relevant Tutorial Fragments for APIs, ICSE'17)

报告人：张静宣

报告人简介:

张静宣，男，大连理工大学博士研究生，博士三年级在读，研究方向为软件仓库挖掘，软件数据分析。2008年到2012年大连理工大学读本科，2012年至今大连理工大学硕博连读，导师为江贺教授。目前已发表论文7篇，包含ICSE 2017，SANER 2016，Internetware 2014等国际会议，SCIENCE CHINA Information Sciences，IJSEKE，Journal of Heuristics，Journal of Computer Research and Development等期刊。2016年获大连理工大学优秀研究生，2015年获大连理工大学博士生一等学业奖学金，2014年到新加坡管理大学做研究助理。

摘要：

Developers increasingly rely on API tutorials to facilitate software development. However, it remains a challenging task for them to discover relevant API tutorial fragments explaining unfamiliar APIs. Existing supervised approaches suffer from the heavy burden of manually preparing corpus-specific annotated data and features. In this study, we propose a novel unsupervised approach, namely Fragment Recommender for APIs with PageRank and Topic model (FRAPT). FRAPT can well address two main challenges lying in the task and effectively determine relevant tutorial fragments for APIs. In FRAPT, a Fragment Parser is proposed to identify APIs in tutorial fragments and replace ambiguous pronouns and variables with related ontologies and API names, so as to address the pronoun and variable resolution challenge. Then, a Fragment Filter employs a set of non-explanatory detection rules to remove non-explanatory fragments, thus address the non-explanatory fragment identification challenge. Finally, two correlation scores are achieved and aggregated to determine relevant fragments for APIs, by applying both topic model and PageRank algorithm to the retained fragments. Extensive experiments over two publicly open tutorial corpora show that, FRAPT improves the state-of-the-art approach by 8.77% and 12.32% respectively in terms of F-Measure. The effectiveness of key components of FRAPT is also validated.

关键字：API，主题模型，PageRank算法，无监督学习

报告题目：数值稳定性相关安全漏洞隐患的自动化检测方法

报告人：沈维军

报告人简介:

沈维军，男，1989年出生，南京大学软件工程专业在读博士生。

摘要：

安全漏洞检测是保障软件安全性的重要手段.随着互联网的发展,黑客的攻击手段日趋多样化,且攻击技术不断翻新,使软件安全受到了新的威胁.本文描述了当前软件中实际存在的一种新类型的安全漏洞隐患,我们称之 为数值稳定性相关的安全漏洞隐患.由于黑客可以利用该类漏洞绕过现有的防护措施,且已有的数值稳定性分析方 法很难检测到该类漏洞的存在,因而这一新类型的漏洞隐患十分危险.面对这一挑战,本文首先从数值稳定性引起软件行为改变的角度定义了数值稳定性相关的安全漏洞隐患,并给出了对应的自动化检测方法.该方法基于动静态相 结合的程序分析与符号执行技术,通过数值变量符号式提取、静态攻击流程分析、以及高精度动态攻击验证三个步骤,来检测和分析软件中可能存在的数值稳定性相关安全漏洞.我们在业界多个著名开源软件上进行了实例研究,实验结果表明,本文方法能够有效检测到实际软件中真实存在的数值稳定性相关漏洞隐患.

关键字：漏洞检测，数值稳定性，程序分析，软件安全

报告题目：待定

报告人：胡星

报告人简介:

胡星，北京大学软件研究所博士生，从事软件代码自动生成技术的研究。

摘要：

对软件生成方法的最新进展及发展状态进行汇报。

关键字：代码自动生成技术

报告题目：待定

报告人：王文翰

报告人简介:

王文瀚，北京大学软件研究所博士研究生。

摘要：

对代码错误自动预测技术的基本进展进行汇报和研讨。

关键字：代码错误自动预测

报告题目：需求变更技术债务研究

报告人：张云洁

报告人简介:

张云洁，女，1994年出生，云南昆明人，云南大学软件学院软件工程技术研究生，研究方向：软件需求（主要是软件需求债务方向）。西南大学本科，软件工程专业，云南大学软件学院硕士生在读。

摘要：

在软件生命周期中，需求不断发生变更，而需求决策往往取决于开发人员的偏好和权衡，缺乏一种系统的、明确的管理方法。针对软件生命周期中不断出现的需求变更而引起的技术债务提出了一种需求变更技术债务定义。通过对需求变更技术债务的定义、检测、量化和排序，为需求变更的实现顺序以及实现方式提供技术支持。最后通过实验验证了需求变更技术债务概念和技术的可行性。

关键字：技术债务，需求变更，需求变更技术债务

报告题目：Mining Feature-Opinion from Reviews Based on Dependency Parsing

报告人：郝蕊

报告人简介:

郝蕊，目前于南京大学软件学院攻读博士学位，导师为陈振宇教授。研究兴趣为众包测试、软件分析。

摘要：

Manually read all the product reviews to find a satisfying item is not only labor-intensive, but also tedious for the consumers. In this paper, we propose a feature-opinion mining approach to automatically summarize the reviews, which is based on dependency parsing. Specifically, in our approach we first utilize a regression model to generate sentiment word, including phrase and its sentiment weight, and then we extract feature based on the dependency relationship between feature word and sentiment word, finally we assign score to feature according to the dependency relationship. The experimental results demonstrate that our approach can effectively mine the feature-opinion from reviews.

关键字：

报告题目：面向航天数传测试任务的通用虚拟仪器关键技术研究

报告人：刘文兴

报告人简介:

刘文兴，男，西北工业大学在读硕士研究生，导师，郑炜副教授。导师研究方向：软件测试/软件质量保证。研究旨在通过程序分析、人工智能、云计算、虚拟化等技术提高软件可靠性。

摘要：

航天应用系统较传统软件来说，有着更高质量、更高可靠性和更高稳定性要求，但航天应用系统接口繁多，测试流程纷繁复杂，往往引起测试不充分、应用系统可靠性无法保证等问题。因此，迫切需要一种通用自动化测试系统。课题研究源自于《通用数传自动化测试及判读系统》自动化测试项目，作为该项目的关键技术，研究旨在建立可拆卸的通用虚拟仪器对象模型，在此基础上，研究一种面向安全和并行特性的通用工作流引擎技术。
针对建立可拆卸的通用虚拟仪器对象模型问题，课题研究提出一种组件技术实现虚拟对象模型。多样的设备由种类不多的组件组成。将组件不断抽象，顶层抽象组件持久化，用户接口灵活定义组件关系形成虚拟仪器。
在虚拟仪器基础上，为实现航天领域内具备安全和并行特性的通用工作流引擎技术，利用异步并行的Petri网技术将航天测试任务流图形化建模，形成实时柔性的通用工作流模型，进而形成面向航天级安全与高并发性的通用工作流引擎。
通过课题研究成果的项目级应用，验证了课题研究的正确性与可行性。

关键字：虚拟仪器，Petri网，工作流引擎