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“By 2028, a large portion of code wil be generated by AI rathern than humans.”

- McKinsey & Company

The future of humans
in an AI-first QAT
organization

We all share the same goal in software testing and QA: to deliver applications and software that perform as intended, eliminate risk, and optimize business and customer outcomes.

AI represents an unprecedented new way—not just to achieve that goal— but to redefine it.

Software quality and assurance (SQA) and testing will be AI-first. Tens of millions of testers work using antiquated, costly, and slow scripts and recorders—AI will replace them and many of their tools in the next five years.

Testing coverage will shift to include automation of the current 100% of specified test cases – but also address the remaining 90% of likely user fellows with validations not originally specified. Every Enterprise will aggressively pursue AI testing and QA automation as a key pillar of its AI-driven IT transformation.

Andrej Karpathy, a former research scientist at OpenAI and now Director of AI at Tesla, predicts that “in ten years, most software jobs won’t involve programming” as AI systems become capable of creating software by learning from data. He envisions a future where “a large portion of programmers of tomorrow do not maintain complex software repositories, write intricate programs, or analyze their running times” but instead focus on collecting, cleaning, and manipulating data to feed neural networks.

As AI continues to evolve and automate many aspects of software testing, humans will likely take on new roles that leverage their unique abilities in conjunction with AI capabilities.

Five potential new roles for humans in software testing and quality assurance include:

01

AI Trainer
and Validator:

Humans will be crucial in training AI models for automated testing. This involves creating training datasets, defining test cases, and validating the accuracy and reliability of AI-generated tests. Human testers will ensure that AI systems understand complex scenarios and edge cases correctly, refining AI algorithms based on real-world feedback and changing requirements.

02

Ethics and
Bias Analyst:

With AI playing a significant role in testing, experts must ensure ethical considerations and mitigate biases in automated testing processes. Humans will oversee AI decision-making processes, ensuring fairness, transparency, and adherence to regulatory standards. They will also audit AI algorithms to prevent unintended consequences and ensure unbiased and inclusive testing processes.

03

Strategic Test
Planner:

While AI can execute tests efficiently, human testers will specialize in strategic planning and prioritization of testing efforts. They will analyze business requirements, customer feedback, and market trends to develop comprehensive test strategies. These testers will decide which areas require more rigorous testing, balancing risk and cost-effectiveness to maximize test coverage and effectiveness.

04

User Experience
Tester:

Human testers evaluate software from a user-centric perspective, ensuring that AI-generated tests reflect real user behaviors and expectations. They perform usability testing, accessibility assessments, and user acceptance testing to validate that software meets user needs and delivers a seamless experience. This role involves understanding user personas, feedback analysis, and translating user insights into actionable testing strategies.

05

Complex Scenario Investigator:

Despite AI’s capabilities, certain complex scenarios and intricate system interactions may still require human expertise to identify, replicate, and test thoroughly. Human testers will specialize in investigating edge cases, rare bugs, and unusual user behaviors that AI may overlook. They will use creative problem-solving skills and domain knowledge to uncover hidden defects and ensure robust software quality.

The Rapid Evolution Of SQA

These roles demonstrate how human testers can complement AI automation by providing critical oversight, strategic guidance, ethical assurance, user-centric validation, and expertise in complex testing challenges.

As AI takes on routine tasks, humans will focus on higher-level activities requiring human intuition, creativity, and domain expertise, enhancing overall software quality and reliability.

The impact of AI on QAT will be evolutionary with impacts emerging over time as AI-first strategies take hold.

The impact of AI on QAT

Short-term
(within the next 5-10 years):

  • AI systems are expected to play an increasingly significant role in assisting software developers with tasks such as code generation, code optimization, bug detection, and code maintenance.

  • AI-assisted development tools and platforms are likely to become more prevalent, leveraging techniques like machine learning and natural language processing to enhance productivity and code quality.

  • However, human developers will still be essential for tasks that require higher-level reasoning, creativity, and decision-making.

Medium-term
(10-20 years):

  • According to some experts, AI systems could potentially generate a significant portion (up to 50% or more) of the code for certain types of applications, particularly those with well-defined specifications and requirements.

  • AI-generated code may become more reliable and efficient, reducing the need for extensive manual coding in some domains.

  • However, human oversight and involvement will likely still be required for critical applications, system design, and overall project management.

Long-term
(beyond 20 years):

  • Some researchers and technologists envision a future where AI systems could potentially generate most of the code for many types of applications, with human developers focusing primarily on high-level architecture, requirements gathering, and overseeing the AI-driven development process.

  • This scenario assumes significant breakthroughs in areas such as artificial general intelligence (AGI), natural language understanding, and the ability of AI systems to comprehend and translate complex requirements into executable code.

  • However, this is a highly speculative and uncertain prediction, as the development of AGI is an extremely challenging task with no clear timeline.

Our vision

Delivering the Era of the AI-first Enterprise

Accelerating digital transformation outcomes through AI-first software innovation and quality. Ensuring enterprises drive structural change and cost reduction by exploiting AI-first QAT strategies that eliminate $100 billion of expenditure.

Who we are

Appvance is not just an innovator in AI-first QAT, but a trusted leader – we are pioneering the only complete AI-first software QAT platform, providing our clients with a secure and confident path to digital transformation.

Our mission​

To accelerate Enterprise transformation and innovation at a lower cost and risk through AI-first software & application delivery.​

Leading the journey to AI-first QAT​

As the first to market in 2018, Appvance continues to pioneer the journey to AI-first QAT through four pillars:​

01

Continous innovation

Continuous innovation, in which technologies build on each other, enable Enterprises to start the journey to AI-first QAT today, knowing they are provided with a pathway to its full potential.

02

AI first

Proving the power of AI-first QAT through rigorous R&D – cutting through the AI-hype cycle with provable outcomes.

03

Enterprise
QAT at scale

A concentrated effort on Enterprise QAT at a scale where maximum cost savings and outcomes can be delivered.

04

QAT
leadership

Assisting QAT leaders in evolving their organizations for an AI-first world.

The future of software quality assurance & testing.

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