Context QA leverages context-aware AI to automate repetitive QA tasks, integrate with tools like Jira, and align testing with business goals, delivering high ROI while addressing industry challenges like siloed teams and fragile automation. The approach emphasizes hybrid AI-human collaboration, shared QA responsibility, and adaptive skills for testers to focus on strategic, judgment-driven testing in evolving DevOps environments.

How To Test This?

interview episodes where Mamadou N’diaye talks with with software testing experts

• https://podcasters.spotify.com/pod/show/spidey1944
• https://www.youtube.com/@HowToTestThis
• https://www.linkedin.com/in/mamadou-ndiaye-consultant/

Episode Details

• Show Notes: https://podcasters.spotify.com/pod/show/spidey1944/episodes/How-To-Test-With-ContextQA---Deep-Barot-e3ib22f
• Published: 2026-04-23T04:50:00Z
• Duration: 00:48:27
• Author: Mamadou N’diaye

Overview

The podcast discusses the evolution of software testing through the lens of Deep Bharat, a software engineer who transitioned into QA after identifying industry pain points such as siloed teams, manual processes, and inadequate automation. He founded Context QA, a company focused on context-aware AI solutions to address these challenges, emphasizing the need for accessible automation tools that reduce repetitive tasks and empower teams to focus on judgment-driven testing. Key themes include the shortcomings of traditional QA methods, such as reliance on fragile tools like Selenium, and the importance of integrating contextual factorsproduct, design, development, and DevOps perspectivesinto testing frameworks to ensure comprehensive coverage. The approach aims to align QA practices with business outcomes, streamline CI/CD pipelines, and address resource constraints like limited documentation and prioritization of automation.

A central focus is the role of AI in transforming QA, advocating for a hybrid model where AI handles 99% of repetitive tasks, while humans manage edge cases and provide strategic oversight. The podcast highlights Context QAs framework, which uses AI to automate test case generation, organize tests by priority, and integrate with tools like Jira for end-to-end lifecycle management. This approach is shown to deliver significant ROI (12x20x) by reducing release cycles and improving defect tracking. Challenges include overcoming team resistance to AI, ensuring secure and privacy-compliant use of AI models, and avoiding misconceptions such as treating AI as a standalone solution. The discussion also underscores the shift toward a shared QA responsibility across teams and the need for QA professionals to adapt by learning AI-specific skills while retaining problem-solving and collaboration expertise.

The podcast explores broader industry shifts, including the move from headcount-based QA consulting to outcome-driven models and the importance of aligning AI adoption with business goals. It advises QA professionals to embrace AI as a tool to enhance, rather than replace, human expertise, emphasizing adaptability, context-aware testing, and practical skills like understanding AI limitations and integrating tools into existing workflows. Additionally, it addresses the evolving role of testers in 2026, encouraging a product-owner mindset, AI proficiency, and a focus on solving business problems rather than just technical tasks. The conversation concludes with practical strategies for tool selection, team collaboration, and fostering a culture of continuous learning in QA roles.

What If

• What if you implemented AI-driven test case generation using context-aware frameworks to reduce manual effort?
  Move: Integrate a tool like Context QA to automate test case creation from PRDs, design mockups, and code logic, prioritizing edge cases and negative scenarios.
  Why now: Legacy tools like Selenium are fragile and time-consuming; AI can handle 99% of repetitive tasks, freeing you to focus on strategic test design and judgment.
  Expected upside: Achieve 90%+ automation coverage in 2 months, cutting release cycles in half while ensuring comprehensive test coverage across product modules.

• What if you leveraged cross-functional context (product, design, DevOps) to refine your testing lifecycle?
  Move: Create a shared documentation hub where developers, designers, and product managers input updates (e.g., API changes, UX adjustments) directly into your test management system.
  Why now: Siloed teams often miss critical context updates, leading to test gaps and production defects. Integrating this data ensures alignment with business goals and real-world usage.
  Expected upside: Reduce bug-fixing rework by 40% and improve collaboration, as testers and developers share a unified view of application changes and priorities.

• What if you audited your current QA workflow to shift 80% of tasks to AI automation, reserving 20% for human judgment?
  Move: Map your QA tasks (e.g., test execution, regression checks) to AI tools like Context QA, then manually oversee edge cases and exploratory testing.
  Why now: Rigid manual testing is outdated; AI can scale efficiently, while your role evolves to prioritize problem-solving and collaboration with engineers.
  Expected upside: Free up 20 hours/week for high-impact work (e.g., automation strategy, mentoring), while achieving 1220x ROI through faster, context-aware testing cycles.

Takeaway

• Adopt Context-Aware AI Testing Tools: Integrate tools that combine product, design, development, and DevOps perspectives to ensure tests cover broader contextual factors (e.g., user journeys, code logic, and analytics) and reduce gaps caused by siloed teams.

• Automate 80% of Repetitive Tasks with AI: Use AI-driven automation to handle ~99% of test case generation and execution, freeing 20% of your time for judgment-driven tasks like collaboration, problem-solving, and strategic planning.

• Migrate Existing Test Cases to AI Platforms: Leverage AI tools to import test cases from Jira, spreadsheets, or code repositories, achieving initial 60% automation coverage, and scale incrementally with AI agents adding ~30-35% more coverage over six weeks.

• Apply the 80/20 Rule to QA Workflow: Allocate 80% of your time to high-impact, judgment-based activities (e.g., aligning with product goals, debugging with context-rich logs) and minimize manual work to 20% by relying on AI for repetitive execution.

• Prioritize Business-Driven Test Metrics: Focus automation efforts on outcomes (e.g., reducing production issues, improving CI/CD efficiency) rather than vanity metrics like test case quantity; use tools that link test coverage to ROI and defect prevention.