In Part 1 , we explored how prioritizing speed over human-centric design in Agile can lead to quality issues. We discussed various reasons that create quality issues, such as :

1. Lost knowledge between the ‘Requirements phase’ and the ‘Scrum phase’
2. Design Thinking is not fully extended to the Scrum framework
3. Scrum teams often neglecting the principles of Design Thinking  
4. Testing within the same SPRINT being a persistent challenge  
5. Teams miss Design -à Teams miss testing of Complex and Edge cases  

In this part 2 blog, we will spend some time on ‘Generative Design + Automation’ and ‘No-Code Automation powered by AI, LLMs, and SLMs’ to reconcile speed and quality.

Balancing Speed and Quality: Key Recommendations for Agile Teams

1. Generative Design + Automation

Don’t overlook design. Many teams, especially Developers and Testers, skip this critical phase due to time pressure. While emerging technologies like AI are quite supportive, and can assist in design generation, it’s important to maintain human oversight.

In this way, by actively engaging in the design and testing processes, teams can ensure their product meets user expectations and adheres to quality standards, even within tight sprint timelines. This collaborative approach ensures swift feedback loops, early detection of issues, and necessary adjustments to the final product (before the release).  

2. No-Code Automation Powered by AI, LLMs, and SLMs

Manual testing remains a very popular & essential testing method, especially for complex, high-risk products that are challenging to automate. While Code-based tools like Selenium and Cypress have been widely adopted, the rise of Low-Code and No-Code tools/platforms has made testing easier. No-code tools, in particular, have gained significant momentum in recent times, as they don’t need extensive coding knowledge from users. However, No-Code tools are still in their early stages of development and adoption. The excitement around Large Language Model (LLMs) and Small Language Models (SLMs) continues to grow. These AI-driven technologies have the potential to transform the software testing, though they currently serve more as supporting tools rather than standalone solutions. As these technologies evolve, they may play a more prominent role in reshaping the industry.

A hybrid approach of ‘No-Code tool with AI capabilities, like those enabled by LLMs’ can empower teams to generate test cases and automate their execution within a single sprint

No-Code Test Automation can be a powerful solution for various challenges in Software testing :

As the software footprint expands and the talent gap widens, No-Code Test automation would emerge like a critical enabler. By empowering non-technical teams to create and execute tests, organizations can accelerate their development cycles, improve software quality, and maintain a competitive edge.

1. Accelerated Testing : By streamlining the test creation and execution process, No-Code test automation tools/platform significantly reduce testing time and enable faster release cycles.
2. Wider Test Coverage : No-Code test platforms empower teams to create and execute a wider range of tests, including complex scenarios, which leads wider test coverage and higher quality software.
3. Democratized Testing : No-Code test automation tools & platform make test automation accessible to a broader range of individuals including non-technical testers. This democratization of testing enables organizations to scale their testing efforts and achieve greater efficiency.

Introducing Muffins: A No-Code AI-Powered Test Automation Platform

Muffins offers a suite of tools that enable Agile teams to incorporate Design Thinking principles throughout the development lifecycle. By prioritizing design at each phase, Muffins helps organizations achieve both speed and quality.

Muffins’ Approach : Leverage ‘Generative Design + Automation’ for Optimal Speed and Quality

Agile success is not just about automation; Test Design that relies heavily on human intelligence is equally critical.   While human involvement in test design is essential, it can slow down the process. To accelerate this, we at Muffins propose a two-step approach :

1. AI-Generated & Human-Reviewed Test Design: Use Generative design tools to automate the creation of test cases, as human testers can review and refine these designs; The role of Tester shifts from ‘being a test writer’ to ‘being a test reviewer’.

2. Automated Execution and Defect Management: Once test designs are finalized, their execution and defect management are fully automated. This allows testers to focus on high-value activities, like analyzing complex scenarios and edge cases, while the system manages routine tasks like test scheduling, execution, and defect tracking.  

The downstream tasks of automation and defect management are more mechanical and repetitive – therefore, they are ideal for automation. By automating repetitive tasks and leveraging AI-powered tools, we can boost efficiency and ensure better test coverage, ultimately improving product quality.

Testers should prioritise tasks where human intelligence is crucial

Testers often struggle to keep up with Agile’s rapid development cycles, faster feedback loops, and frequent feature releases.  Traditional testing methods, requiring extensive manual effort for requirement analysis, test case design, execution, and defect management, are time-consuming and inefficient.

How do we solve this?

1. Prioritize Human-Centric Activities

Critical thinking, creativity, and domain expertise can make agile development successful . Test cases crafted by domain experts are essential to meeting customer expectations and avoiding critical defects.  

2.Leverage Automation for Repetitive Tasks  

Automation can streamline routine testing tasks but cannot replicate the empathy, creativity, and intuition of human testers. A balanced approach allows testers to focus on strategic and high-impact activities.