Will Wilson wants to make sure the software running everything from your bank account to your favorite crypto exchange actually works—and his company Antithesis is rethinking how software has been tested for the last 80 years.​​ Wilson, the co‑founder and CEO of Antithesis, first made his name at FoundationDB, a company that created special testing systems that let teams safely rehearse years of real‑world problems in a fake environment, to catch bugs before customers ever saw them (FoundationDB was acqui ...

实验室地址：广东省佛山市顺德区杏坛镇顺业西路15号中集智能制造中心22栋802厂房（住所申报） (3)错误群集原则。软件错误呈现群集现象。经验表明，某程序段剩余的错误数目与该程序段中已发现的错误数目成正比，所以应该对错误群集的程序段进 行重点测试。 (4)严格性原则。严格执行测试计划，排除测试的随意性。 (5)覆盖原则。应当对每一个测试结果做全面的检查。 (6)定义功能测试原则。检查程序是否做了要做的事仅是成功的一半，另一半是看程序是否做了不属于它做的事。 (7)回归测试原则。应妥善保留测试用例，不仅可以用于回归测试，也可以为以后的测试提供参考。 国家实验室资质CNAS编号L23122，省级实验室资质CMA编号202419120184 广东省宝通质量检测有限公司 测试用例由测试输入数据和与之对应的预期输出结果这两部分组成。 3.独立测试原则 (1)独立测试原则。这是指软件测试工作由在经济上和管理上独立于开发机构的组织进行。程序员应避免检查自己的程序，程序设计机构也不应测试自己开 发的程序。软件开发者难以客观、有效地测试自己的软件，而找出那些因为对需求的误解而产生的错误就更加困难。 (2)合法和非合法原则。在设 ...

（来源：新华日报） □ 本报记者 许雯斐 实习生 王若宁 上传一段汇编代码，敲下运行键，不到10秒钟，一份分析报告完成。这份报告包含漏洞识别、异常逻辑 定位等多项检测内容。 2月11日，南京江北新区科学城的一栋大厦内，南京云上华安科技有限公司创始人周宇，只用几秒钟就 完成了1名资深程序员至少要花十几分钟才能完成的检测任务。 "这要归功于我们的'新同事'，团队开发的AI智能体，它效率惊人！"周宇笑着指向屏幕，这位年轻创业 者初步估算了一下，AI智能体在公司应用后，能顶上3—5个程序员的工作量。 这是一家专攻软件质量与安全测试、硬件虚拟化技术的创新企业。公司成立于2023年初，目前团队仅6 人，全员技术出身，市场拓展、行政事务等工作几人合力一肩挑。 三四年间，团队日夜兼程、埋头攻关，研发出多款技术领先行业的软件测试产品。其中，名叫布尔黑盒 智能模糊测试平台的一款产品，就曾发现苹果公司某软件中的数个高危漏洞，收获了苹果公司的致谢公 告。 当AI大模型的浪潮席卷而来，这群严谨的技术青年没有盲目跟风，而是带着一连串问题观望思考："除 了对话，大模型能在各行业有什么实际应用？检测结果准确吗？数据安全有保障吗？"这份谨慎， ...

Core Insights - Momentic, an AI-driven software testing startup, raised $15 million in Series A funding led by Standard Capital, with participation from Dropbox Ventures and existing investors [3] - The company aims to simplify and automate the software testing process, traditionally dominated by open-source frameworks like Playwright and Selenium, by leveraging AI technology [3][4] - Momentic has gained traction with 2,600 users from notable companies such as Notion, Xero, and Webflow, and has automated over 200 million test steps in the past month [4] Funding and Growth - The recent funding round builds on a previous seed round of $3.7 million announced in March [3] - The company is focused on enhancing its product with the new funding, including mobile environment support and plans for a more complex test case management system [5] Market Position and Competition - Momentic's AI-driven solution addresses a significant pain point in code validation, which has been a challenge for many development teams [4] - The main competition for Momentic may come from foundational models like those offered by OpenAI and Anthropic, which provide advanced capabilities for automated testing [4] Future Outlook - The rise of automated coding is expected to create a surge in demand for testing solutions, as all new applications will require quality assurance [6]

Core Insights - Keysight Technologies, Inc. has been recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for AI-Augmented Software Testing Tools, highlighting its strong position in the market [1] Company Summary - The recognition is based on an evaluation of technology providers regarding their vision and execution in the AI-driven testing solutions market [1] - The Gartner® Magic Quadrant™ is a trusted resource for technology decision-makers, aiding in the assessment and differentiation of vendors in key technology segments [1]

Core Insights - TestGrid has launched CoTester for ServiceNow, the first testing agent specifically designed for the ServiceNow ecosystem, enabling end-to-end testing with adaptive AI and zero-maintenance execution [1][5] Group 1: Product Features - CoTester for ServiceNow automates the testing process, generating test cases from workflows and healing broken scripts in real time, ensuring continuous quality and faster release cycles [2][3] - The solution utilizes a multi-modal AI engine that analyzes screens, text, and layout to detect subtle UI changes, while its self-healing module, AgentRx, updates scripts automatically [4] - Key capabilities include AI-powered test generation, automatic self-healing during upgrades, prebuilt test libraries, and seamless integration with popular DevOps tools [6] Group 2: Performance Metrics - Early adopters report significant improvements, including a 45% faster upgrade validation and a 60% reduction in maintenance effort, with zero workflow disruption during platform updates [7] - The solution offers up to a 70% reduction in test maintenance effort, 50% faster upgrade validation cycles, and 40% lower overall testing costs [6] Group 3: Market Impact - CoTester for ServiceNow is already deployed across Fortune 100 enterprises in sectors such as banking, telecom, healthcare, and eCommerce, expanding its reach in critical automation spaces [5][7] - The product is compatible with both standard and heavily customized ServiceNow instances, making it versatile for various enterprise needs [7]

Core Insights - Software performance testing and stress testing are distinct yet complementary aspects of software quality assurance, focusing on different objectives and execution methods [1][3]. Performance Testing - Performance testing evaluates software performance under specific load conditions, measuring response time, throughput, and resource consumption [1]. - It simulates normal or peak user access to ensure the system meets predefined requirements, such as testing a website's ability to handle 100 concurrent requests [1]. - This type of testing is conducted throughout the development cycle and can be applied to individual modules or the entire system [1]. Stress Testing - Stress testing assesses system stability and fault recovery under extreme load conditions by incrementally increasing the number of concurrent users or data volume until the system fails [1]. - For example, it may involve increasing simulated users from 50 to 5000 to identify when server response degrades or services are interrupted [1]. - This testing specifically aims to identify system bottlenecks and crash thresholds [1]. Testing Scope and Output - Performance testing covers various load scenarios, including normal operation and fatigue state validation, while stress testing focuses on overload environments [3]. - Performance testing generates response time curves and resource utilization reports, whereas stress testing records failure points and system recovery times [3]. Tool Utilization - Performance testing tools include LoadRunner for monitoring transaction processing rates and JMeter for analyzing concurrent request throughput [3]. - Stress testing tools like Gatling create high-load impacts, and Tsung simulates distributed access pressure [3]. - A specific case involved a financial platform project where customized scripts were used to replicate memory leak issues during peak transaction volumes [3]. Execution Order - Performance testing is prioritized to ensure baseline performance standards are met, followed by stress testing to validate system resilience [3].

Core Viewpoint - TestSprite 2.0 is an innovative AI testing platform designed specifically for AI programming, significantly improving code accuracy from 42% to 93% and enabling the creation of new websites in just 30 minutes without human intervention [2][19][13]. Group 1: Product Features - TestSprite is the first testing platform tailored for AI programming, allowing users to initiate testing with a simple prompt in their IDE [3][8]. - The platform automatically reviews product requirement documents, descriptors, and code libraries to generate comprehensive integration test plans [9]. - TestSprite can autonomously generate all necessary test cases, write test code, compile test scripts, execute tests in a cloud infrastructure, and return structured reports to coding agents [12]. Group 2: Performance and Impact - The platform's performance was particularly impressive on the Trae development platform, demonstrating its capability to test, debug, and fix errors efficiently [11][13]. - The entire process of building a complete website with zero code was achieved in just 30 minutes, showcasing the platform's efficiency [13][15]. - TestSprite has gained the trust of over 6,000 development teams, indicating strong market acceptance and demand [21]. Group 3: Company Background - TestSprite was founded by Yunhao Jiao, a Zhejiang University alumnus with a strong background in natural language processing and software development [25][31]. - The company aims to reduce software release cycles by up to ten times by eliminating cumbersome manual testing processes [31]. - In November 2024, TestSprite secured $1.5 million in seed funding from top investment firms, which will help scale its autonomous testing tools [32][33].