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Most Common Challenges In Automation Testing & Their Solutions

Automation testing can help you deploy the software but before that it is necessary to overcome common challenges in automation testing. Below we will highlight some of those challenges.
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1How LLMs are Reshaping Finance: AI Applications & Case Studies Explore how top banks like ICBC, CCB, and CMB are leveraging LLMs (DeepSeek, Qwen) for wealth management, risk control, and operational efficiency. A deep dive into the financial AI ecosystem.
2Testing Considerations for Location-Based Apps This article will discuss key considerations for testing location-based apps, including GPS and network testing, battery consumption, permissions and privacy, geofencing and proximity alerts, integration with mapping services, performance testing, and usability.
333 LLM Evaluation Metrics: A Complete Guide for 2026 | Performance, Quality & Cost Learn how to evaluate Large Language Models with 33 essential metrics covering latency, output quality, safety, and cost. Includes a practical learning roadmap for AI engineers and testers.
4Advancing Test Quality | Foundations (4): Testing in the SDLC—Comparing Waterfall, Agile, and DevOps Methodologies Learn how software testing adapts to Waterfall, Agile, and DevOps models. Discover strategies for "Shift-Left" and "Shift-Right" testing, automated CI/CD integration, and hybrid SDLC approaches to maximize quality in the 2026 tech landscape.
5How to Confirm the Performance of Mobile Game - Network Performance Testing (Part 3) In this article, we will explore the importance of network performance testing and highlight key considerations for optimizing connectivity and gameplay.
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