The AI Advantage in QA: Automate Smarter, Not Harder

Automation Evolution

The transition to automation has been around for over a decade, with tools like Appium and Selenium. Traditionally, this process involved writing step-by-step test scripts for every interaction:

Detailing what a feature should do
Defining the ideal outcome
Identifying errors if the ideal outcome wasn’t met

These scripts, managed by Software Development Engineers in Testing (SDETs), required significant effort and time, particularly for products with numerous interactions, underscoring the challenges of traditional automation. For instance, a simple text field interaction might involve 4-5 steps, but when testing complete workflows, these can add up to a hundred. This made maintaining automation increasingly difficult, especially for rapidly evolving products with frequent updates.

In many cases, traditional automation methods became inefficient and costly, pushing teams back toward manual testing. These approaches primarily catered to large, stable products with predictable workflows, where the frequency of changes was low. For smaller or more dynamic teams, these solutions couldn’t keep up, leaving teams still dependent on manual testing.

The Transformative Shift to AI

Traditional automation in QA faced significant inefficiencies, particularly due to the heavy workload involved in creating test scripts. These scripts, which required meticulous detailing of each interaction, created a bottleneck that hindered automation from reaching its full potential. Logically, if the process of creating these test scripts was the most time-consuming and cumbersome task, why not automate that first?

AI agents represent a transformative step in addressing this challenge. By taking over the creation of these test scripts autonomously and intelligently adapting to specific contexts, AI dramatically reduces the workload traditionally carried by SDETs. Quash also uses this approach, equipping its agents with tools to ensure context awareness and smooth integration into dynamic workflows.

AI agents handle repetitive tasks, reducing reliance on expensive SDETs.
These agents operate autonomously yet intelligently, requiring minimal human intervention for context-specific decisions.
By blending AI-driven efficiency with human oversight, automation becomes a viable option for even the most dynamic products.

Context Awareness: The Cornerstone of Effective Automation

In QA automation, context is everything. Without it, test scripts become rigid and limited—capable of executing predefined actions but unable to adapt to real-world scenarios. Context-aware testing addresses this by ensuring test scripts are dynamic, relevant, and capable of handling nuanced interactions within a product.

Gathering this context is a key challenge. While there are several methods available, some are more efficient than others. For instance, prompt-to-test requires users to explicitly provide every detail for the AI to process—a time-consuming and often inefficient approach, especially for complex workflows.

A more advanced method is Retrieval-Augmented Generation (RAG). In this approach, the system retrieves relevant information from a vector database, typically stored as vector embeddings, based on a given query. This retrieved context then guides the AI in generating accurate and informed test scripts. While RAG is powerful, it comes with limitations. Since it relies on embedding searches for context retrieval, which might not always capture the semantic meaning of words, this can lead to irrelevant or inaccurate context. These models have limited token capacities, which restrict the amount of context that can be processed. Poor-quality context or overly long paragraphs can cause hallucinations, where the AI generates incorrect or nonsensical outputs.

Another highly effective method is leveraging Knowledge Graphs. These use Resource Description Frameworks (RDF) to define entities (nodes) and their relationships (edges). Knowledge graphs have distinct advantages:

They represent relationships explicitly through subject-object-predicate structures, ensuring context relevance and accuracy.
They dynamically adapt to changes, evolving alongside the product. For instance, if a feature is added or updated, the knowledge graph can reflect this, maintaining accuracy in dynamic environments.

Quash utilizes knowledge graphs as a foundation to enhance the training of its AI agents. By exploring these knowledge graphs, the agents gather context more effectively, enabling the creation of test scripts that are not only accurate but also aligned with real-world use cases. Furthermore, the dynamic nature of knowledge graphs ensures that test scripts remain relevant as products evolve. While context awareness is a cornerstone of effective QA automation, it is an area that requires continuous improvement, highlighting the need for ongoing refinement and innovation in automation tools.

A Gradual Transition

Transitioning from manual testing to automation can be challenging for teams accustomed to traditional processes. However, a gradual adoption strategy can ease the shift and drive success. To achieve this, teams must aim to:

Automating up to 80% of testing processes within 2-3 months.
Allowing teams to maintain their current workflows while progressively offloading tasks to AI.
Urge and ensure collaboration between developers and QA teams to evaluate and refine the automation tool, making it more effective and aligned with team needs.

Automation tools should seamlessly integrate with existing processes, offering both scalability and efficiency without disrupting the team’s workflow.

The Vision for Automation

Cutting time while maintaining quality, QA automation strives to make testing a seamless background operation—efficient, reliable, and requiring minimal manual intervention. Automation is about evolving your testing processes, eliminating repetitive tasks, and enabling your team to focus on high-value priorities. With the right approach, testing can become an enabler of progress—not a bottleneck.

Yes, automation has its limitations today. We know that 100% automation isn’t achievable with current technology. Complex scenarios, edge cases, and critical decisions will always require human judgment. By adopting context-aware AI solutions and building incrementally, teams can advance meaningfully. But here’s the truth: waiting for perfect automation gets you nowhere. The longer you wait, the further you fall behind.

Don’t wait for perfection—start building toward it, now.

New Talent and AI Insights @ Quash!

This week, we’re thrilled to welcome Anindya Srivastava, Abrar Ahmed, and Donna Dominic to the Quash team!

Anindya Srivastava – Product Designer

Fun Fact: I’m convinced that no matter how serious the situation is, a pinch of pun and humour is always required, and I love to be the one to add it.

Hi, I’m Anindya! I come from a background in creating immersive 3D environments for AAA game titles, but my passion for design has led me to transition into product design. I’m always looking to push the boundaries of what’s possible in design, whether it’s in gaming or building innovative products. Along with that, I’ve had the chance to design some cool stickers and T-shirts for the company, and trust me—there’s much more to come!

Abrar Ahmed – AI Engineer

Fun Fact: If you ever catch me talking to myself, don’t worry—I’m not an exorcist, I’m just coding!

Hi, I’m Abrar! With a strong background in deep learning, reinforcement learning, and web development, I’m excited to shape the future of AI here at Quash. I’m passionate about building smarter, more efficient systems and love diving into the complexities of AI to solve real-world challenges.

Donna Dominic – Content Marketing Strategist

Fun Fact: I love cats and am absolutely crazy about them—though I don’t have one (yet!). Recently, I’ve also been getting into cooking—just a little! I find AI is better than YouTube for discovering new recipes, so I’m letting tech help me become a master chef. Who knew AI could be my sous-chef?

Hey, I’m Donna! I come from a background in PR and have worked with startups, so I’m all about crafting compelling stories that help growing brands connect with their audience. I want to help Quash’s story reach and resonate with the right people. Also, I’m the one leading the charge on these newsletters ;)

“

Humans are multimodal, the world is designed for humans — therefore, AI needs to be multimodal."

This statement perfectly captured the spirit of AI Day Bengaluru, attended by our teammates Mahak and Prakhar. Hosted by E2E Startup Programs, the event offered incredible insights into the future of AI.

Highlights included discussions on architecture of NExT-GPT, an any-to-any multimodal LLM capable of cross-modal transformations like converting sound into video, and tools such as Llama 3, Qwen VL, Extractors.ai, and Kyut AI. The speakers agreed with our teammates as we dived into the biggest challenge of AI startups—long term context.

It was a great day of learning, and we’re inspired to continue pushing the boundaries of AI-driven QA!

Meet Our AI Experts + The Future of Automated Testing 🚀