论软件评审在军用软件质量控制中的作用

为了说明软件评审在军用软件质量控制中所起的重要作用,对评审和测试的有效性进行了比较,并对目前我国军用软件领域中的评审实施不利的原因进行了深入的剖析,最终得出结论,软件评审对军用软件产品的质量有至关重要的影响,只有采用正确的方法进行评审,并将软件评审和软件测试相结合,才能真正提高我国军用软件的质量.     

独立学院专业评审中权重关系的研究

重庆市对独立学院进行专业评审,该文是从对独立学院专业评审指标体系的一级指标进行分析,从各指标之间的关系进行比较,得出了各一级指标之间的权重关系。这与各指标的分值没有分歧,权重是从整体的情况来进行分析,明确指标在专业评审中的地位。     

软件评审

软件评审是软件质量控制的一个重要手段．本文简要介绍了软件评审目标，过程和准则等内容，希望能从除度量与测试以外的角度来对电子政务系统建设中软件质量进行控制。     

一种有效的软件评审模型

软件评审是软件开发过程中的一个重要步骤,有助于提高产品质量、降低生产成本和提高生产率.为了更好发挥软件评审在软件开发中的作用,对软件评审中涉及的活动和过程进行了描述,提出了根据开发流程采用技术评评审和管理评审的分层模型,讨论了评审与研发组织结构、评审实践中存在的误区和解决措施.为了检验提出的评审模型,在10个软件项目进行了初步应用,取得了较好的评审效果.     

浅谈软件评审

文章介绍了一般软件评审涉及的内容和软件评审中的各个角色与职责,重点说明软件评审的过程及评审的标准和评审中要注意的问题。同时也着重阐述了不同的软件开发可以根据此评审过程进行裁剪,评审过程是帮助作者提前发现评审对象的缺陷,而不是对评审对象的发布执行。     

军用软件评审标准的改进措施研究

软件评审在军用软件的开发中占有重要地位.但是,我军的软件评审标准仅来源于IEEE的标准,这对于有着高安全性和高稳定性要求的军用软件是不够的,为了改进我军软件评审标准,提高评审在军用软件中的可操作性,本文先对几种航天行业软件评审标准进行对比介绍,然后分别分析这些软件评审标准的不同之处,最后对我军软件评审标准进行一些改进,...     

基于PCA-BP模型的高校教师职称评审预测

针对高校教师职称评审问题,提出基于PCA-BP的评审预测模型.采用主成分分析法对评审指标数据进行降维处理,选取保留原始指标信息的89.01％的四个主成分作为BP网络的输入,这样不仅减少了网络的输入维数,减小了网络训练规模,而且消除了各指标间的相关性,改善了网络的训练效率,提高了预测精度.利用Matlab软件对某高校2012年副教授评审实际数据的进行实例分析和仿真,并用该组数据比较该方法与典型BP网络的预测效果,结果表明该方法明显优于BP网络,完全能够满足职称评审预测的要求.     

软件过程中同行评审的应用与度量

在软件过程中采用同行评审可以及早、高效地发现软件缺陷,从而广泛、深入且有效地吸收和应用软件过程信息,同时评价和提升软件过程能力水平.为了在软件过程中更加有效地应用同行评审,以CMM/CMMI为基础,详细分析了软件过程中同行评审的方法,并介绍了同行评审的流程.在此基础上,对如何建立和实施同行评审的度量进行了研究和分析,并提出了利用同行评审的度量数据建立组织同行评审过程能力基线的方法.     

软件项目的同行评审

软件评审是软件项目中重要的环节,与软件测试共同构建了软件开发的保障体系.列出了发现错误的时间与软件成本和开发风险的关系,分析了同行评审的种类和对象,指出了可行的评审过程,并总结了项目开发过程中评审容易出现的错误.通过同行评审,能够及早识别并消除缺陷,让软件交得更易维护,通过对这些错误的分类和统计,发现共同的缺陷类型和修...     

CMM/CMMI中同行评审子过程的定量控制

为了解决实施CMM／CMMI过程中对软件过程的定量控制问题，进而实现对软件质量、成本和进度的定量管理，引进计算数学中的“线拟合”方法。该方法描述了互相牵制的定量指标之间的数学关系，弥补了经典的SPC技术只能分析单个度量的不足。将“曲线拟合”法用于对“同行评审”子过程的定量控制，实践证明，它较好地刻画了该子过程两个定量指标之间的关系，使得“同行评审”的结果在任何时候都是精确受控的。     

Linguistic characteristics of shill reviews

This exploratory study investigates the linguistic characteristics of shill reviews and develops a tool for extracting product features from the text of product reviews. Shill reviews are increasingly used to manipulate the reputation of products sold on websites. To overcome limitations of identifying shill reviews, we collected shill reviews as primary data from students posing as shills. Using semi-automated natural language processing techniques, we compared shill reviews and normal reviews on informativeness, subjectivity and readability. The results showed evidence of substantial differences between shill reviews and normal reviews in both subjectivity and readability. Informativeness appears to be a mixed separator of shill and normal reviews so additional studies may be necessary. Overall, the study provides improved understanding of shill reviews and demonstrates a method to extract and classify features from product reviews with an eventual goal to increase effectiveness of review filtering methods.     

Software reviews, the state of the practice

A 2002 survey found that many companies use software reviews unsystematically, creating a mismatch between expected outcomes and review implementations. This suggests that many software practitioners understand basic review concepts but often fall to exploit their full potential.     

Software architecture review by association

During the process of software design, software architects have their reasons to choose certain software components to address particular software requirements and constraints. However, existing software architecture review techniques often rely on the design reviewers’ knowledge and experience, and perhaps using some checklists, to identify design gaps and issues, without questioning the reasoning behind the decisions made by the architects. In this paper, we approach design reviews from a design reasoning perspective. We propose to use an association-based review procedure to identify design issues by first associating all the relevant design concerns, problems and solutions systematically; and then verifying if the causal relationships between these design elements are valid. Using this procedure, we discovered new design issues in all three industrial cases, despite their internal architecture reviews and one of the three systems being operational. With the newly found design issues, we derive eight general design reasoning failure scenarios.     

The effectiveness of software development technical reviews: abehaviorally motivated program of research

Software engineers use a number of different types of software development technical review (SDTR) for the purpose of detecting defects in software products. This paper applies the behavioral theory of group performance to explain the outcomes of software reviews. A program of empirical research is developed, including propositions to both explain review performance and identify ways of improving review performance based on the specific strengths of individuals and groups. Its contributions are to clarify our understanding of what drives defect detection performance in SDTRs and to set an agenda for future research. In identifying individuals' task expertise as the primary driver of review performance, the research program suggests specific points of leverage for substantially improving review performance. It points to the importance of understanding software reading expertise and implies the need for a reconsideration of existing approaches to managing reviews     

What Types of Defects Are Really Discovered in Code Reviews?

Research on code reviews has often focused on defect counts instead of defect types, which offers an imperfect view of code review benefits. In this paper, we classified the defects of nine industrial (C/C++) and 23 student (Java) code reviews, detecting 388 and 371 defects, respectively. First, we discovered that 75 percent of defects found during the review do not affect the visible functionality of the software. Instead, these defects improved software evolvability by making it easier to understand and modify. Second, we created a defect classification consisting of functional and evolvability defects. The evolvability defect classification is based on the defect types found in this study, but, for the functional defects, we studied and compared existing functional defect classifications. The classification can be useful for assigning code review roles, creating checklists, assessing software evolvability, and building software engineering tools. We conclude that, in addition to functional defects, code reviews find many evolvability defects and, thus, offer additional benefits over execution-based quality assurance methods that cannot detect evolvability defects. We suggest that code reviews may be most valuable for software products with long life cycles as the value of discovering evolvability defects in them is greater than for short life cycle systems.     

轻型评论的情感分析研究

以在智能移动设备上发表的用户评论作为研究对象,并将该类评论称为轻型评论。指出了轻型评论与早期互联网评论及短文本研究的异同点,并通过实验总结轻型评论的独有特性：字数少、跨度大,短小评论数量众多,评论长度与数量满足幂率分布。同时,针对轻型评论的情感分类研究展开了一系列的实验研究,发现：(1)情感分类效果随着评论长度的增加而下降;(2)传统的特征筛选方法以及特征加权方法对于轻型评论效果都不够理想;(3)极性词在短评论中比例高于长评论;(4)长、短评论在用词上存在较高的重叠度。在此基础上,提出了一种基于短评论特征共现的特征筛选方法,将短小评论中的优势信息和传统的特征筛选方法相结合,在筛选掉无用噪音的同时增补有利于分类的有效特征。实验结果表明,该方法可以有效地提高轻型评论中较长评论的分类效果。     

Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an average accuracy of 96%. The experimental results obtained with various M&DL classifiers are encouraging. It is the first step towards identifying fake reviews in the app store by studying its implications in software and requirements engineering.     

A formal methods approach to medical device review

With software playing an increasingly important role in medical devices, regulatory agencies such as the US Food and Drug Administration need effective means for assuring that this software is safe and reliable. The FDA has been striving for a more rigorous engineering-based review strategy to provide this assurance. The use of mathematics-based techniques in the development of software might help accomplish this. However, the lack of standard architectures for medical device software and integrated engineering-tool support for software analysis make a science-based software review process more difficult. The research presented here applies formal modeling methods and static analysis techniques to improve the review process. Regulation of medical device software encompasses reviews of device designs (premarket review) and device performance (postmarket surveillance). The FDA's Center for Devices and Radiological Health performs the premarket review on a device to evaluate its safety and effectiveness. As part of this process, the agency reviews software development life-cycle artifacts for appropriate quality-assurance attributes, which tend to reveal little about the device software integrity.