软件抄袭检测研究综述

随着开源软件项目的蓬勃发展,软件抄袭俨然已成为软件生态环境健康发展的威胁之一,其得到越来越多的研究人员、教育人员、开源社区及软件企业的关注,软件抄袭检测对于软件知识产权保护具有重要意义。本文对软件抄袭检测的研究现状和进展进行综述。首先介绍软件抄袭检测的意义和威胁模型;然后,根据应用场景和技术手段,从源代码抄袭检测、无源码场景下基于软件水印和基于软件胎记的抄袭检测三个方面,对现有软件抄袭检测技术进行阐述和比较;最后,通过分析软件抄袭检测研究存在的问题及其面临的挑战和实际需求,对未来研究方向进行了展望。     

开源C/C++静态软件缺陷检测工具实证研究

软件静态缺陷检测是软件安全领域中的一个研究热点.随着使用C/C++语言编写的软件规模和复杂度的逐渐提高, 软件迭代速度的逐渐加快, 由于静态软件缺陷检测不需要运行目标代码即可发现其中潜藏的缺陷, 因而在工业界和学术界受到了更广泛的关注.近年来涌现大量使用软件静态分析技术的检测工具, 并在不同领域的软件项目中发挥了不可忽视的作用, 但是开发者仍然对静态缺陷检测工具缺乏信心.高误报率是C/C++静态缺陷检测工具难以普及的首要原因.因此, 我们选择现有较为完善的开源C/C++静态缺陷检测工具, 在Juliet基准测试集和37个良好维护的开源软件项目上对特定类型缺陷的检测效果进行了深入研究, 结合检测工具的具体实现归纳了导致静态缺陷检测工具产生误报的关键原因.同时, 我们通过研究静态缺陷检测工具的版本迁移轨迹, 总结出了当下静态分析工具的发展方向和未来趋势, 有助未来静态分析技术的优化和发展, 从而实现静态缺陷检测工具的普及应用.     

IBFET: Index-based features extraction technique for scalable code clone detection at file level granularity

Many techniques have been developed over the years to detect code clones in different software systems to maintain security measures. These techniques often require the source code to compare the subject system against a very large data set of big code. This paper presents index-based features extraction technique (IBFET) to detect code clones at a very large-scale level to billions of LOC at file level granularity. We performed preprocessing, indexing, and clone detection for more than 324 billion of LOC using a Hadoop distributed environment, which is quite faster and more efficient as compared to existing distributed indexing and clone detection techniques; meanwhile, it detects all three types of clones efficiently. The MapReduce rule of divide and conquer is used for a count and retrieve the similar features between different systems. We evaluated the execution time, scalability, precision, and recall of IBFET by using a well-known clone detection data set IJaDataset and BigCloneBench; furthermore, we compared the results with other state-of-the-art tools. Our approach is faster, flexible, scalable, and provides accurate results with high authenticity and can be implemented at a large-scale level.     

Improving software security with static automated code analysis in an industry setting

Software security can be improved by identifying and correcting vulnerabilities. In order to reduce the cost of rework, vulnerabilities should be detected as early and efficiently as possible. Static automated code analysis is an approach for early detection. So far, only few empirical studies have been conducted in an industrial context to evaluate static automated code analysis. A case study was conducted to evaluate static code analysis in industry focusing on defect detection capability, deployment, and usage of static automated code analysis with a focus on software security. We identified that the tool was capable of detecting memory related vulnerabilities, but few vulnerabilities of other types. The deployment of the tool played an important role in its success as an early vulnerability detector, but also the developers perception of the tools merit. Classifying the warnings from the tool was harder for the developers than to correct them. The correction of false positives in some cases created new vulnerabilities in previously safe code. With regard to defect detection ability, we conclude that static code analysis is able to identify vulnerabilities in different categories. In terms of deployment, we conclude that the tool should be integrated with bug reporting systems, and developers need to share the responsibility for classifying and reporting warnings. With regard to tool usage by developers, we propose to use multiple persons (at least two) in classifying a warning. The same goes for making the decision of how to act based on the warning. Copyright © 2012 John Wiley & Sons, Ltd.     

代码质量静态度量的研究与应用

代码质量度量是软件质量分析的一个重要研究方向。静态分析方法因其具有成本低、容易实现而且不依赖于程序特定的运行环境的优点,在当前软件网络化、服务化的趋势下倍受关注。针对Java代码质量度量进行研究,使用Ant工具整合各种开源的静态测试工具,并制定基于静态分析的Java代码质量综合评价方案,可支持包括代码规模、规范性、可维护性、可扩展性和潜在危险等方面的综合检测,为项目的开发者、管理者和使用者提供了实用的代码质量评价方法。     

An analysis technique to increase testability of object‐oriented components

Object‐oriented component engineering is increasingly used for system development, partly because it emphasizes portability and reusability. Each time a component is used, it must be retested in the new environment. Unfortunately, the data abstraction that components usually use results in low testability. First, internal variables cannot be directly set. Second, even though a test input may trigger a fault, the failure does not propagate to the output. This paper presents a technique to increase object‐oriented component testability, thereby making it easier to detect faults. Components are often sealed so that source code is not available. The program analysis is performed at the Java component bytecode level. A component's bytecode is analysed to create a control and data flow graph, which is then used to increase component testability by increasing both controllability and observability. We have implemented this technique and applied it to several components. Experimental results reveal that fault detection can be increased by using our increasing testability process. Copyright © 2008 John Wiley & Sons, Ltd.     

Investigating ARIMA models of software system quality

In this paper, we investigate how to incorporate program complexity measures with a software quality model. We collect software complexity metrics and fault counts from each build during the testing phase of a large commercial software system. Though the data are limited in quantity, we are able to predict the number of faults in the next build. The technique we used is called times series analysis and forecasting. The methodology assumes that future predictions are based on the history of past observations. We will show that the combined complexity quality model is an improvement over the simpler quality only model. Finally, we explore how the testing process used in this development may be improved by using these predictions and suggest areas for future research.     

Effort, co-operation and co-ordination in an open source software project: GNOME

Abstract This paper presents results from research into open source projects from a software engineering perspective. The research methodology employed relies on public data retrieved from the CVS repository of the GNOME project and relevant discussion groups. This methodology is described, and results concerning the special characteristics of open source software development are given. These data are used for a first approach to estimating the total effort to be expended.     

基于程序控制流图源代码相似程度分析系统

源代码相似程度分析在软件工程和计算机教学等领域都有重要的应用.软件工程领域的源代码盗窃和著作权纠纷仲裁,计算机教学领域的学生作业作弊分析都需要源代码相似程度的分析.良好的源代码相似程序分析软件还可以应用于相似代码聚类和搜索引擎的源代码搜索领域.尽管源代码相似程度分析问题由来已久,但是这个问题并没有令人十分满意和惊喜的研究结果.源代码有其特殊结构,使用传统的纯文本相似度分析显然是不合适的.将首先介绍这个问题的研究历史和进展,简单分析这个问题的难点所在,继而介绍一个新的基于程序控制流图分析的源代码相似程度分析系统,并给出其算法和实现细节.文章最后将分析这个方法的优劣所在,讨论这个方法的进一步改进方向.     

Comparative performance evaluation of applying extended PIE technique to accelerate software testability analysis

The rapid development of technology provides high performance and reliability for the hardware system; based on this, software engineers can focus their developed software on more convenience and ultra-high reliability. To reach this goal, the testing stage of software development life cycle usually takes more time and effort due to the growing complexity of the software. How to build software that can be tested efficiently has become an important topic in addition to enhancing and developing new testing methods. Thus, research on software testability has been conducted and various methods have been developed. In the past, a dynamic technique for estimating program testability was proposed and called propagation, infection and execution (PIE) analysis. Previous research studies have shown that PIE analysis can complement software testing. However, this method requires a lot of computational overhead in estimating the testability of software components. In this article, we propose an extended PIE (EPIE) method to accelerate the conventional PIE analysis, based on generating group testability as a substitute for statement testability. Our proposed method can be systematically separated into three steps: breaking a program into blocks, dividing the blocks into groups and marking target statements. Experiments and evaluations with the Siemens suite, together with cost-effectiveness analysis, clearly show that the number of analysed statements can be effectively decreased, and the calculated values of testability are still acceptable.     

综合包级和类级度量的软件缺陷预测方法

在基于软件产品度量值的缺陷预测中,度量值主要是基于两个层次:类/文件层次和包/组件层次。类级别的预测模型通常会有更好的预测效率,而包级别的模型往往能得到更好的查全率及查准率。提出综合类级别和包级别度量值进行缺陷预测的方法,在类级别预测的基础上,使用包级别预测的信息对类级别进行调整,在类级别预测中融合包级别预测中所隐含的问题域信息。通过基于Eclipse3.0系统的实验发现,该方法能够有效改善缺陷预测的效果。与类级别的缺陷预测模型相比,综合包级别度量值的缺陷预测方法提高了5%到8%的查全率。同时在预测效率上,测试出50%的缺陷,使用该方法可以有效减少3.6%到9.84%的代码检查量。     

基于深度聚类的开源软件漏洞检测方法

针对开源软件漏洞,提出一种基于深度聚类算法的软件源代码漏洞检测方法。该方法利用代码图模型构造开源软件代码属性图,遍历得到关键代码节点并提取出应用程序编程接口（API）序列,将其嵌入向量空间,以关键代码为中心进行聚类,根据聚类结果计算每个函数的异常值,生成检测报告并匹配漏洞库,从而检测出源代码中的漏洞。实验结果表明,该方法能够定位开源软件中漏洞所在的关键代码段并检测出相应漏洞。     

ATS中TPS的软件测试技术研究

自动测试系统(ATS)的软件是整个系统的核心,而软件测试是保证TPS质量的关键要素.在总结TPS的软件测试经验的基础上,给出了TPS的软件测试模型,分析了TPS软件测试中遇到的相关技术,并且在ATE8000等项目开发中取得了良好的效果.     

白盒测试中源代码变更管理方法的研究与实现

在大型软件白盒测试项目中,源代码的频繁变化给测试工作增加了很大难度,对源代码进行管理和控制是对白盒测试过程管理的一个重要补充。在已有的软件测试过程管理模型基础上,认真分析研究版本控制和管理的相关技术现状,提出了变更管理的改进方法,对于软件模块的比较采用了抽象的方法简化为目录树,而在文件比较中基于源代码静态分析工具,把文件比较的粒度分解到方法一级,进行了方法内部比较算法的研究,设计实现了源代码版本管理子系统。     

程序代码相似度度量算法研究

代码剽窃是程序设计课程中经常出现的一种作弊行为,检测剽窃的源代码,验证学生程序作业的原创性在教学中很重要.程序代码的相似度度量是剽窃检测的关键技术.通过对现有程序代码相似度度量技术进行研究后,基于Karp-Rabin和最长公共子串算法思想,提出了一种改进的源代码相似度度量算法,即串的散列值匹配算法.     

源代码分析技术的理论与实践发展

源代码分析技术对于软件安全缺陷分析是一项非常重要的手段.分析了软件源代码分析工具的技术手段和发展过程,最后对源代码分析的理论和实践进行了分析总结.     

空指针引用故障模型与测试方法研究

空指针态引用故障在使用指针的程序中是普遍存在的,采用动态测试方法进行测试难以准确定位故障源。而现有的静态分析方法主要存在漏报和误报过多的情况。针对这些问题,提出了一种基于指针映射关系分析的测试方法,给出了面向故障的指针映射集的构造规则,以此为基础建立了故障模型。通过指针映射集和故障模型,可以自动检测空指针引用内存故障,提高了测试效率。在分析过程中,还综合应用了控制流图和路径条件,提高了测试结果的精度。     

一种基于LDA和静态分析的代码功能识别方法

近年来,随着代码复用技术不断成熟和Internet上开源项目不断丰富,软件开发人员的开发行为也逐渐发生了变化。如今,软件开发人员在编程过程中越来越多地依赖于开源软件项目提供的功能。然而,在软件复用活动中,由于开源项目文档的不全面以及代码结构的复杂性,软件开发人员往往只能片面地了解项目的某些功能点,使得复用效率不高。针对开源项目代码丰富而文档较少这一现状,提出了一种基于LDA（Latent Dirichlet Allocation）和静态分析的代码功能识别方法,对传统LDA方法进行了扩展,帮助软件开发人员更全面地了解项目的功能点,从而更好地支持代码复用活动。     

SPOON: A library for implementing analyses and transformations of Java source code

This paper presents SPOON , a library for the analysis and transformation of Java source code. SPOON enables Java developers to write a large range of domain‐specific analyses and transformations in an easy and concise manner. SPOON analyses and transformations are written in plain Java. With SPOON , developers do not need to dive into parsing, to hack a compiler infrastructure, or to master a new formalism. Copyright © 2015 John Wiley & Sons, Ltd.