Software Testing Background

本文探讨了软件错误的历史案例,定义了软件缺陷,并分析了缺陷产生的原因及成本。此外,还讨论了如何成为一名优秀的软件测试人员。

Software Testing Background
Chapter 1
2

In this chapter
 Infamous software error case studies
 What is a bug?
 Why do bugs occurs?
 The cost of bugs
 What makes a good software tester?
3

Story of Bug
 Mark II, Harvard University, 1947
 Behemoth stopped working
 A moth stuck between a set of relay contacts
 Computer BUG was born
4

Infamous software error case studies
 Disney Lion King, 1944-1945
 Intel Pentium floating-point division bug, 1994
 NASA Mars Polar Lander, 1999
 Patriot Missile Defense System, 1991
 The Y2K (Year 2000) Bug, circa 1974
 Dangerous Viewing Ahead, 2004
5

What is a Bug
 Software failure
 The software didn’t operate as intended
 Terms for Software Failure
 Defect, Variance, Fault, Failure, Problem,
Inconsistency, Error, Feature, Incident, Bug,
Anomaly
 Why should we know these terms?
6

Software Bug: A Formal Definition
 Product specification
 Software bug occurs when:
 The software doesn’t do something that the
spec says it should do
 The software does something that the spec
says it shouldn’t do
 The software does something that the spec
doesn’t mention
 The software is difficult to understand, hard to
use, slow
7

Why do Bugs Occur
 Most of the bugs aren’t caused by
programming errors
 Number one: Specification
 Number two: Design
 Number three: Coding error
 Number four: Others
8

The Cost of Bugs
 The cost are logarithmic
$1
$10
$100
$1000+
specification design code release
9

What Exactly Does a Software Tester Do?
 The goal of software tester is to find bugs
 The goal of software tester is to find bugs and
find them as early as possible
The goal of a software tester
is to find bugs,
find them as
early as possible
,
and make
sure they get fixed
 Fixing a bug does not necessarily imply
correcting the software
10

What Makes a Good Software Tester?
 Question: Is tester’s job easier than a programmer’s?
 “As a matter of cosmic history, it has always been easier to destroy
than to create.”
 List of traits for software testers:
 They are explorers
 They are troubleshooters
 They are relentless
 They creative
 They are perfectionists
 They exercise good judgment
 They are tactful and diplomatic
 They are persuasive
 A fundamental trait of software testers is
they simply like to break things. 

Li Zhang, Wei-Tek Tsai, Adaptive attention fusion network for cross-device GUI element re-identification in crowdsourced testing, Neurocomputing, Volume 580, 2024, 127502, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2024.127502. (https://www.sciencedirect.com/science/article/pii/S092523122400273X) Abstract: The rapid growth of mobile devices has ushered in an era of different device platforms. Different devices require a consistent user experience, especially with similar graphical user interfaces (GUIs). However, the different code bases of the various operating systems as well as the different GUI layouts and resolutions of the various devices pose a challenge for automated software testing. Crowdsourced software testing (CST) has emerged as a viable solution where crowdsourced workers perform tests on their own devices and provide detailed bug reports. Although CST is cost-effective, it is not very efficient and requires a large number of workers for manual testing. The potential of optimizing CST reproduction testing through computer vision remains largely untapped, especially when considering the uniformity of GUI elements on different devices. In this study, we present a novel deep learning model specifically designed to re-identify GUI elements in CST reproduction test scenarios, regardless of the underlying code changes on different devices. The model features a robust backbone network for feature extraction, an innovative attention mechanism with learnable factors to enhance the features of GUI elements and minimize interference from their backgrounds, and a classifier to determine matching labels for these elements. Our approach was validated on a large GUI element dataset containing 31,098 element images for training, 115,704 element images from real apps for testing, and 67 different background images. The results of our experiments underline the excellent accuracy of the model and the importance of each component. This work is a major step forward in improving the efficiency of reproduction testing in CST. The innovative solutions we propose could further reduce labor costs for CST platforms. Keywords: Crowdtesting; Computer vision; Convolutional neural network; GUI element re-identification; Adaptive attention mechanism; Cross-device testing
03-22
<think>好的,我现在需要处理用户提供的关于如何引用一篇特定论文的请求。用户之前已经询问过如何引用ScienceDirect上的论文,现在给出了具体的论文信息,希望得到正确的引用格式。首先,我需要确认用户的需求是什么。看起来用户可能是在撰写学术论文或报告,需要按照特定的引用格式引用这篇名为《Adaptive attention fusion network for cross-device GUI element re-identification in crowdsourced testing》的文章。用户已经提供了详细的文献信息,包括作者、标题、期刊、卷号、页码、ISSN、DOI等,所以我的任务是根据这些信息生成不同格式的引用,如APA、MLA、IEEE等。 接下来,我需要回忆不同引用格式的要求。APA格式通常要求作者姓氏和首字母,出版年份,文章标题(仅首字母大写),期刊名称斜体,卷号斜体,期号在括号内,页码,然后是DOI。MLA格式则不同,作者全名,文章标题用引号,期刊名称斜体,卷号和期号,出版年份,页码,最后是DOI。IEEE格式更注重简写期刊名称,作者首字母在前,姓氏在后,文章标题,期刊名称缩写斜体,卷号,期号,页码,出版年份,以及DOI。 我需要检查用户提供的信息是否完整。作者是Li Zhang和Wei-Tek Tsai,标题正确,期刊是Neurocomputing,卷580,年份2024,页码127502,ISSN和DOI都正确。这些信息足够生成正确的引用。需要注意的是,页码部分在APA格式中可能需要调整,因为有些期刊使用文章编号而不是传统页码,这里显示的是页码127502,所以直接使用即可。 然后,我需要确保各个引用格式的细节正确无误。例如,APA中的作者名字格式是否正确,MLA中的“et al.”使用是否合适(这里只有两位作者,所以应该列出全部),而IEEE通常对三位以上作者使用“et al.”,但两位作者需要全部列出。此外,期刊名称的缩写是否正确,例如Neurocomputing的缩写可能需要确认,但根据常见的缩写规则,可能保持原样或简写为Neurocomput.。 另外,用户提供的链接和DOI是否一致。用户给出的DOI是10.1016/j.neucom.2024.127502,而链接是https://www.sciencedirect.com/science/article/pii/S092523122400273X,需要确认两者是否对应。不过根据常规,DOI是唯一的,所以应该没问题,可能在生成引用时只需使用DOI即可。 还要注意,用户可能希望引用中包括所有作者,但根据不同的格式,可能需要调整。例如,APA格式中两位作者用“&”连接,而MLA中用“and”。同时,检查期刊名称的斜体是否正确应用,卷号和期号的格式是否正确,例如卷号斜体,期号在括号内不带斜体等。 最后,需要确保示例中的信息与实际提供的信息完全一致,避免任何拼写错误或格式错误。例如,作者姓名的大小写是否正确,标题中的每个单词首字母是否大写,DOI链接是否正确等。此外,还要提醒用户核对实际信息,特别是如果用户需要导出引用,可以使用ScienceDirect的导出功能或文献管理软件来确保准确性。</think>以下是针对论文 **Li Zhang, Wei-Tek Tsai, *Adaptive attention fusion network for cross-device GUI element re-identification in crowdsourced testing*(自适应注意力融合网络在众包测试中的跨设备GUI元素重识别)** 的引用方法及摘要总结: --- ### **引用格式示例** 根据学术规范,常用格式如下: #### **1. APA格式** ```text 作者. (出版年). 论文标题. 期刊名, 卷(期), 页码. DOI ``` **示例**: Zhang, L., & Tsai, W.-T. (2024). Adaptive attention fusion network for cross-device GUI element re-identification in crowdsourced testing. *Neurocomputing, 580*, 127502. https://doi.org/10.1016/j.neucom.2024.127502 ``` #### **2. MLA格式** ```text 作者. "论文标题." 期刊名, 卷号, 出版年, 页码. DOI ``` **示例**: Zhang, Li, and Wei-Tek Tsai. "Adaptive Attention Fusion Network for Cross-Device GUI Element Re-Identification in Crowdsourced Testing." *Neurocomputing*, vol. 580, 2024, p. 127502. https://doi.org/10.1016/j.neucom.2024.127502. ``` #### **3. IEEE格式** ```text 作者, "论文标题," 期刊名缩写, 卷号, 页码, 出版年, DOI. ``` **示例**: L. Zhang and W.-T. Tsai, "Adaptive attention fusion network for cross-device GUI element re-identification in crowdsourced testing," *Neurocomputing*, vol. 580, p. 127502, 2024. doi: 10.1016/j.neucom.2024.127502. ``` --- ### **摘要总结** 该论文提出了一种**自适应注意力融合网络**,用于解决众包测试(CST)中跨设备图形用户界面(GUI)元素的重识别问题。主要贡献如下: 1. **研究背景**: - 移动设备多样化导致跨平台GUI元素布局、分辨率差异显著,传统众包测试依赖人工操作,效率低且成本高。 - 现有方法未充分利用计算机视觉技术自动化优化测试流程。 2. **模型设计**: - **主干网络**:提取GUI元素的鲁棒特征。 - **自适应注意力机制**:通过可学习权重增强元素特征,抑制背景干扰。 - **分类器**:判断元素是否匹配(跨设备场景)。 3. **实验验证**: - 数据集:包含31,098张训练图像、115,704张测试图像(来自真实应用)及67种背景。 - 结果:模型在准确率上表现优异,各组件(如注意力机制)对性能提升显著。 4. **意义**: - 为众包测试提供自动化解决方案,减少人工成本,提升测试效率。 - 关键词:众包测试、计算机视觉、卷积神经网络、GUI元素重识别、自适应注意力机制。 --- ### **注意事项** 1. **核对信息**: - 确认期刊卷、期、页码是否与论文页面一致(部分期刊仅标注文章编号)。 - DOI需以`https://doi.org/`开头(APA格式强制要求)。 2. **自动生成工具**: - 在论文页面点击“Export”(ScienceDirect支持BibTeX、EndNote等格式导出)。 - 使用文献管理工具(如Zotero)直接抓取元数据生成引用。 如有其他格式需求(如Chicago、Harvard),可提供具体规范进一步补充!
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