Bridging the Gap: How Gen AI Can Accelerate Testing to Match Development Speed

In today's fast-paced digital landscape, software plays a critical role in virtually every business. The evolution of software, from mainframes to microservices and cloud computing, has led to an unprecedented increase in delivery speed and software production. With AI further accelerating this velocity, maintaining software quality becomes paramount.

DORA metrics, a widely used measure of software performance, focus on both speed (Deployment Frequency and Lead Time for Changes) and quality (Change Fail Rate and Mean Time To Restore). Lead Time for Changes (LTC), the time between code commit and production deployment, is particularly crucial. However, testing often becomes a bottleneck in increasing the deployment frequency and reducing LTC.

Testing speed is significantly slower than development speed, often by a factor of two or more. This disparity arises due to several factors:

• Imbalance in Resources: There is often a significant difference between the number of software development engineers (SDEs) and software development engineers in testing (SDETs) within scrum teams.

• Limited Adoption of TDD: Not many organizations consistently implement Test Driven Development (TDD) or Acceptance TDD (ATDD).

• Automation Challenges: While automation has matured, it requires additional skills and maintenance, and encompasses various types of testing (unit, functional, integration, performance, end-to-end), each with its own set of tools and skill requirements.

• Market Pressures: Organizations are constantly under pressure to reduce software production costs due to intense competition.

• Wait Times: Testers often wait for development to complete before starting, leading to delays and frustration for product managers eager to release to customers.

• Regression Suite Growth: As software evolves, the regression suite grows, increasing the time needed for execution and delaying releases.

However, Generative AI offers a promising solution to bridge this gap and accelerate testing. Gen AI can be used in all stages of testing, including:

• Generating test cases from user stories

• Creating automated tests (unit, functional, performance, front-end)

• Identifying and running only necessary tests from the regression suite based on the changes specific to user stories

• Analyzing test failures and pinpointing code issues

These advancements can be achieved at greater speed without requiring a large increase in team size. While the use of Gen AI in testing is still in its early stages, combining Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) can significantly improve the quality of results.

If you are looking to accelerate your testing process and leverage the power of Gen AI, reaching out to Nomiso is a step in the right direction.