The Fifth Elephant
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The Fifth Elephant Pune edition

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Abhishek Prasad Singh

@a4automation

Future-Proofing QA: AI Strategies for Faster, Reliable Testing

Submitted Sep 15, 2025

Future-Proofing QA: AI Strategies for Faster, Reliable Testing

With AI accelerating software development, teams are shipping firmware, mobile apps, web front-ends, and backend services faster than ever — yet testing remains constrained by linear resources, outdated test frameworks, and the steep learning curves faced by existing QA teams. The hurdles range from expensive hardware setups and scarce device pools to fragile automation across phones, browsers, and embedded rigs, all of which grow more demanding in end-to-end scenarios such as firmware-to-cloud validation, mobile apps orchestrating IoT devices, or browser dashboards linked to physical hardware. This widening gap between exponential development speed and linear testing capacity risks making validation the bottleneck. In this session, we will explore how embedding AI into legacy test frameworks can bridge that gap — enabling faster, smarter, and more reliable testing across firmware, apps, and browsers, while reducing manual overhead and empowering QA to keep pace with AI-driven innovation.

Who is This Session For?

This session is designed for software engineers, test automation specialists, QA leads, DevOps practitioners, and engineering managers across domains such as IoT, consumer electronics, mobile, cloud, and automotive. The audience primarily includes mid-to-senior professionals with 5–20 years of experience who face the challenge of scaling test automation across rapidly evolving firmware, mobile apps, web front-ends, backend services, and distributed hardware setups. They are especially interested in approaches that improve testing speed, scalability, and reliability while reducing manual overhead and enabling QA to keep pace with AI-driven innovation.

What We Will Cover

The session will focus on modernizing legacy test automation workflows with AI across firmware, applications, web front-ends, backends, and hardware setups. It will cover:

  • Architectural principles for embedding AI into legacy test frameworks while maintaining stability and scalability.
  • AI-driven strategies for log analysis, anomaly detection, failure classification, and intelligent result interpretation.
  • Balancing test cycles in multi-dimensional development, where hardware, firmware, mobile apps, cloud backends, features, and bug fixes all evolve independently and often asynchronously.
  • Data-driven acceleration through the use of historical test data, context sharing, and metadata to deliver predictive insights and faster decision-making.
  • Real-world examples of AI-enabled test agents powering end-to-end scenarios — from firmware-to-cloud validation to mobile app and browser dashboard automation — as well as integrations with open-source frameworks (Matter, Wi-Fi Alliance etc.)

Key Takeaways

By attending, participants will gain:

  • Actionable strategies to modernize their existing test automation frameworks using AI.
  • Practical strategies to accelerate validation and optimize limited hardware setups.
  • Approaches for building AI-assisted workflows that improve scalability and adaptability.
  • Real examples of how AI agents can be applied to open-source embedded test frameworks and in-house systems.
  • A forward-looking view of QA as an enabler of innovation, not a bottleneck in the age of AI-driven development.

About the Speaker

Abhishek Prasad Singh is an Engineering Manager at Espressif Systems, where he leads test automation and QA initiatives for large-scale IoT and embedded solutions. With over 14+ years of experience at NXP Semiconductors, Arista Networks, Mojo Networks, and Marvell Technology, he brings deep expertise in automation architecture, CI/CD integration, and AI-driven QA strategies. Abhishek is passionate about bridging the gap between rapid development and scalable testing by building intelligent frameworks that accelerate validation and reduce manual effort.

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