## Introduction

Artificial Intelligence (AI) has been playing a significant role in the development of new technologies. One of the most popular AI models is the Generative Pre-trained Transformer (GPT), developed by OpenAI. GPT is used in several applications, including chatbots, recommendation systems, and language translation services. However, testing GPT-based services presents several challenges, including the complexity of the models and the lack of a standard testing framework. In this blog post, we will discuss some effective ways of testing GPT-based services.

## Testing Strategies

### 1. Input/Output Testing

Input/Output testing is the most common method of testing GPT-based services. In this method, we provide inputs to the model and compare the outputs with the expected results. This method is effective for testing the correctness of the model's output. However, it does not test the model's behavior in different scenarios.

### 2. Scenario Testing

Scenario testing involves testing the model's behavior in different scenarios. For example, if a chatbot is designed to provide customer support, we can test its behavior in scenarios where the customer asks different types of questions. This method is effective for identifying edge cases and testing the model's behavior in different scenarios. However, it requires a significant amount of effort to design and execute different scenarios.

### 3. Performance Testing

Performance testing is the process of testing the model's performance under different loads. For example, we can test the chatbot's response time under different levels of concurrent users. This method is effective for identifying performance bottlenecks and ensuring that the model can handle the expected load. However, it requires specialized tools and expertise to conduct performance testing.

## Conclusion

Testing GPT-based services presents several challenges, but with the right testing strategies, we can ensure that the model is correct, behaves well in different scenarios, and can handle the expected load. Input/Output testing, scenario testing, and performance testing are effective ways of testing GPT-based services. By investing time and effort into testing, we can improve the quality of GPT-based services and provide a better user experience.

This article discusses effective ways of testing GPT-based services, including input/output testing, scenario testing, and performance testing.

Testing GPT Based Services

In the recent past companies in the field of insurances and banking like
Ottonova, Lemonade and Osca have invested time and money into developing
conversational agents to communicate with their costumers in a higher
quality. According to Grand View Research, the global chatbot market is
expected to reach $1.23 billion by 2025 with a compounded annual growth
rate (CAGR) of 24.3%. Chatbots reduce operating
costs for enterprises and can work in segments such as marketing,
payments and processing, and service. Within the global chatbot market,
approximately 45% of end users prefer chatbots as the primary mode of
communication for customer service inquires. The new
generation of chatbots or conversational agents operates with the help
of machine learning models. The users tend to have a misconception about
the intelligence and abilities of such agents. Conversations may
end in a dialog breakdown which in the end leads to unhappy
users. To overcome this problem companies need to develop
conversational agents with a better language understanding and language
generation. With every new version of an agent testers need to manually
verify different kinds of possible dialog paths for unwanted dialog
breakdowns. The dynamic nature of natural language and the variety of
expressing the same meaning in different ways leads to a problem with
the task of manual testing a conversational agent.

## Motivation

As usage and demand are growing chatbot systems will be used in more and
more domains where they can cause damage for companies, users and third
parties in different contexts, like health information, money
transactions or insurance contracts. Chatbots need to carefully get
tested. The different test scenarios are countless even for very small
domains. Manual testing can only focus on testing a small surface of
every release of a chat system.

An intelligent test system may automate the tedious task of testing a
dynamic chat system. By using predefined test scenarios the system may
plan the interaction between the bot under test and test system itself.
After the end of the conversation the test system will be able to
evaluate if the past conversation has had the same path at it was
planned before.

The planning and generating of text to feed the chatbot is done by the
system based on training and a given task set. After each full run, the
system generates a report which can be used to check the results for
further actions like quality assurance or continuous integration.

I am confident to adopt this approach to other, related domains in the
field of testing for self-learning systems where the success can not be
determined by gaming rules or something similar. In addition the
approach also could allow us to understand what a chatbot has learned
and which data is needed. This can have a big impact on working with
personal data[^1].

## Research Question

My work focuses on the automatic generation, execution and evaluation of
test plans for conversational agents. In addition I will try to transfer
a possible solution into other domains of self learning systems and
investigate it’s applicability.

[^1]: <https://eugdpr.org/>

Chatbots need to carefully get tested. The different test scenarios are countless even for very small domains.

Automated Testing for self-learning Systems

With most modern web frameworks it is possible to develop simple CRUD applications without much effort. In doing so, we
as developers can make use of code generation and scaffolding to develop handsome applications. Also, for people who are
not so tech-savvy or simply like to use interfaces to assemble applications, there are already enough solutions on the
market.

## But what if we don't just want simple management of data, but need to implement complex logics?

Currently, in this case, a lot of code has to be written and tested. Faster feedback cycles allow us to correct wrong
assumptions or misunderstandings faster, but still we are limited in our work. If we scale the team size, there is
increased effort in communication and management of the teams. This is a problem that many companies face and will
continue to face in the future.

## But what could a solution to this look like?

Let's imagine that we could automate parts of the implementation of logic!

Even relatively simple AlphaGo-like AIs could quickly examine billions of possible complex architectures using a
combination of evolutionary design, adaptive pattern matching, and growing libraries of established software building
blocks. AIs may have the ability to learn from past and hypothetical failures: once an approach has a flaw or
vulnerability, the system will then not try again down the road.

## So what will the future look like?

I think the way we develop will change very quickly. We will no longer be poring over algorithms, finding optimizations,
fixing bugs, and doing code reviews. Instead, we'll focus more and more on specifying the problem and letting the
computer search the solution space.

*First published on LinkedIn (https://www.linkedin.com/pulse/die-zukunft-der-softwareentwicklung-cubicl-gmbh/).*

The Future of Software Development - a personal impression

Christian Blank

Testing GPT Based Services

Automated Testing for self-learning Systems

The Future of Software Development

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