Lynchpin’s Post

🌟 "Mastering #ApplicationTesting: Happy Path vs. Negative Path Testing" In modern application testing, it's essential to test both the happy path (when things go right) and the negative path (when things go wrong). Here's why both approaches matter: 🟢 Happy Path Testing ensures core workflows function smoothly, like logging in or completing a purchase. It’s fast, efficient, and guarantees a seamless experience for users following expected behaviors. 🔴 Negative Path Testing goes deeper, handling unexpected inputs and edge cases to identify vulnerabilities and ensure resilience under real-world conditions. 🔑 Balancing happy, negative, and golden paths (optimized workflows) ensures comprehensive coverage. With AI, we can accelerate test creation, simulate diverse user behaviors, and scale testing across complex systems - ensuring faster, more resilient software delivery. Read the full blog here: https://lnkd.in/gNvDeGnJ #SoftwareTesting #TestAutomation #AI #HappyPath #NegativeTesting #DevOps Elias Hoffner Daniel Crowe Denis Borovikov Stanislav Kuznetsov Tariq Valente Eesha Gholap Margarita Kurushina

Beginning my time at Synthesized.io, I wanted to share what we do and some insights from our most recent blog post. Synthesized is a platform that generates high-quality synthetic data for software testing, model training, and data analysis. 🧠 With cutting-edge generative AI technology at its core, Synthesized empowers developers to create diverse and realistic test datasets that closely mirror real-world scenarios. 💡 Our platform enables the implementation of a "shift-left" approach, allowing developers to access high-quality, production-like test data from the early stages of the development cycle. 🌐 By integrating real-world scenarios into testing environments, Synthesized ensures that your software undergoes rigorous testing against various user inputs and conditions. 🔒 Addressing data management challenges is a breeze with Synthesized. Our platform offers robust solutions for ensuring data quality, diversity, and compliance with regulatory standards while maintaining the highest data integrity and security standards. #Synthesized #SoftwareTesting #AI #Innovation

𝗨𝘀𝗶𝗻𝗴 𝗔𝗜 & 𝗠𝗟 𝗳𝗼𝗿 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟰 In software today, staying on top means using smarter technology. We are adding AI and ML to our automation testing, making it more accurate, efficient, and proactive. Our approach doesn’t just run tests—it finds potential issues, learns from data, and keeps your applications at their best. Want to improve your testing with AI? Connect with us to explore the future of smarter testing. Explore our latest Blog! https://lnkd.in/dBhmsAkh #aitesting #machinelearning #automationtesting #sparkleweb #innovation #2024trends

Seamless interactions between a software system are made possible by APIs. Whether it is a simple interface or something more complex, a lot of abstraction details are hidden from users. Because so much effort and planning goes into API design, development, and testing to ensure its security, reliability, and performance, it can sometimes be more efficient to use AI tools for API testing and development. https://lnkd.in/gtCGWRkz #AI #artificialintelligence #development #testing #API #apitesting

<<Postman - API-first AI development Postman differentiates itself by offering a comprehensive platform that combines API development, LLM integration and workflow automation in a user-friendly environment. Its focus on API-first development and the extensive Postman API Network make it a strong choice for developers looking to build AI agents that interact with the broader API ecosystem. Postman’s AI Agent Builder represents a leap forward in API-first AI development. By providing a unified platform for building, testing and deploying AI agents, Postman enables developers to create innovative solutions that leverage the power of APIs and LLMs. The tool aims to simplify API interaction, streamline workflows and enable the creation of intelligent agents capable of performing complex tasks.>>

AI agents- A boon to test automation!!! Coming across Quite often the discussion if AI is going to play a role in Software testing/test automation. Trends, topics, discussion forums, case studies from different domains reveal the utilization already in place. Realization among different group of individuals is evident due to the real use of AI agents in Test automation. AI agents are handy for automating small tasks to complex Scenarios. Utilization of Large language Models(llm) for an example AI agents collaboration with Open AI and anthropic models to perform actions on web pages within seconds. Doubt might arise is it enough and here comes the famous automation library the selenium where the general functions can be created and models can be treated to perform actions on web elements and perform action. Inputs will be a natural language where in a layman can write steps on what the actions the user intended to do on the web application. Benefit is no more scraping the web manually for xPath where in input from user in natural language is going to be the key for actions. Custom frameworks like Magentic, browser-use are in place and free to use(However utilization of open AI is chargeable and depends on how much the usage is) .

🚀 Fine-Tuning LLMs: The Next Frontier in Software Testing As software testers, we're always exploring ways to make testing workflows more efficient, insightful, and precise. Enter fine-tuning Large Language Models (LLMs)—a game-changer for niche domains in testing. Here's how it can revolutionize the way we work: 🔍 What is Fine-Tuning in the Context of Testing? Fine-tuning an LLM involves training it on domain-specific datasets, enabling the model to generate highly relevant, context-aware outputs. For testers, this means leveraging AI to address specific challenges in workflows, tools, or technologies unique to their projects. 🛠️ How Testers Can Use Fine-Tuned LLMs: 1️⃣ Automated Test Case Generation By training an LLM on past project data, testers can generate detailed, domain-specific test cases with minimal effort. This speeds up test creation and ensures relevance. 2️⃣ Bug Classification and Prioritization A fine-tuned LLM can analyze bug reports, classify them accurately, and prioritize them based on their severity or potential impact—saving precious time in triaging. 3️⃣ Enhanced Documentation Whether it’s creating user manuals, API documentation, or test reports, fine-tuned LLMs can draft high-quality content tailored to the software's domain. 4️⃣ Scenario Simulation LLMs trained on industry-specific data can simulate complex user behaviors, helping testers identify edge cases and improve coverage. 5️⃣ Customized Test Data Creation Fine-tuned models can create realistic and context-specific test data, crucial for domains like healthcare, finance, or e-commerce. 🌟 Benefits for Niche Domains: Healthcare: Generate data or simulate scenarios compliant with industry standards like HIPAA. Finance: Analyze and test scenarios involving regulations such as GDPR or PCI DSS. Retail/E-commerce: Predict peak traffic scenarios or identify pricing calculation errors. 🔑 Getting Started: 1️⃣ Gather domain-specific data (historical test cases, bug reports, user scenarios). 2️⃣ Fine-tune an LLM using platforms like OpenAI, Hugging Face, or Azure AI. 3️⃣ Integrate the fine-tuned model into your testing frameworks for real-time insights. 🧠 The Future is AI-Driven Testing As testers, embracing generative AI and fine-tuning LLMs isn’t just an option—it’s the future. It’s about adapting to evolving technologies while keeping quality and efficiency at the core of our workflows. 💬 What are your thoughts on using fine-tuned LLMs in testing? Are you ready to take the leap? Let’s discuss in the comments! #SoftwareTesting #GenerativeAI #QualityAssurance #LLM #InnovationInTesting #AI

Software Testing is in a constant race against time! Delivering high-quality software faster requires a smarter approach. Traditional test automation tools often struggle to keep pace. 😰 Testers are under immense pressure to deliver bug-free software at breakneck speeds. 🐢 Existing Software Testing automation tools can be slow and inflexible, hindering efficiency. 🎯 AI in Testing is the game-changer. Experience the Power of AI: 🛠️ AI automates tedious tasks, freeing testers to focus on strategic test planning and exploratory testing. 🔍 AI in Testing improves test coverage, identifying bugs with greater accuracy and efficiency. 💸 AI-powered testing tools reduce testing time and costs, giving you a significant edge. Take Control of Your Testing Future! Learn more about the transformative power of AI in Testing: https://lnkd.in/gHzrfFkb 🔗 Consider AI-powered testing tools like TestGrid.io and its CoTester, world's first AI for software testing to unlock a new era of efficiency and quality in your software releases: https://lnkd.in/gQ_t2KJu 🚀  #AI #TestAutomation #SoftwareTesting #TestGrid

AI bots and applications are becoming essential, but testing them can be a challenge. That’s where Typemock Isolator 9.3.3 comes in: ✅ Mock complex AI models and services seamlessly ✅ Simulate exceptions and edge cases without real dependencies ✅ Validate decision-making, responses, and error handling Power your AI testing with Typemock. 👉 Learn more https://lnkd.in/dPikHj-j #AI #DotNetDevelopers #UnitTesting #Typemock

AI is changing the game in software testing! Check out my new(2nd) Medium article to understand how AI technologies are being leveraged to improve testing accuracy and efficiency. Here is the link : https://lnkd.in/dcp9R7_W #testing #qatesting #ai_in_testing