In the software community, the emergence of AI has stoked thoughts of both possibility and concern about its impact. For software testers, the critical questions swirling around AI are: “What is the future of testing in a world of AI? Will testers become obsolete?” Coveros CEO Jeff Payne talked with testing and AI expert and CEO of TestersAI, Jason Arbon, to find out what the emergence of AI means for the testing and software community.
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- Repetition In Practice
- Testing Wins Should Come Through Mastery, Not Luck
- Five Sins of Mobile Testing
- Advancing the Craft of Testing: What Have YOU Done Lately?
- The New Ways to Break Mobile and Embedded Software: An Interview with Jon Hagar
- Technical Testing Levels—A Call for Standards in Hiring
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Technical Documentation: Why So Technical?[article] Testing documentation often fails to explain the context, why the product works the way it does, and how it can truly benefit the user. Testing documentation can be technical, yet relatable—it should also be approachable and resonate with its readers. |
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Data Flow Testing: A Comprehensive Guide[article] Data Flow Testing is a structural testing method that analyzes how data is used within a program, focusing on variable usage throughout the code. By tracing the flow of data, it identifies potential errors like uninitialized variables or outdated values, ensuring data reliability and code quality. This method is crucial in applications where accurate data handling is paramount, such as financial systems, medical software, embedded systems, and mission-critical applications. |
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Building Trust in Blockchain: The Role of Smart Contract Testing[article] Smart contracts, the backbone of decentralized applications, hold immense potential for revolutionizing industries. However, their complex nature and irreversible nature necessitate rigorous testing to ensure their reliability and security. This article delves into the essential aspects of smart contract testing, providing insights into best practices, tools, and challenges. |
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These Challenges Should Not Be an Issue for QA Teams Anymore[article] QA teams play a vital role in ensuring software quality, customer satisfaction, and efficient development. However, challenges such as limited testing talent, poor documentation, and lack of automation persist. AI-powered tools can address these issues by automating repetitive tasks, generating documentation, and improving test efficiency. By leveraging AI, QA teams can enhance their effectiveness and contribute to the success of software development projects. |
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Rethinking Our Job as Software Testers: We Should Try to Test as Little as Possible[article] DevOps transforms testing from finding all bugs to prioritizing critical ones. Risk assessment helps testers focus on high-impact areas. Testing can be pre-release (critical issues), post-release (resolvable issues), or out-of-scope (low-impact). Testing should align with business goals and user stories. Data science helps measure and improve software quality. |
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