Generative Agents in Advanced Testing

July 4, 2024 by shahina.s

Introduction

Software testing proves to be one of the most important elements of software development as it assures that the software can function according to what the users and stakeholders want. Nevertheless, artificial testing is costly in terms of the time and human resources privately used in designing, running, and evaluating tests, and identifying, documenting, and rectifying defects. Besides, software testing is also well-known to be time-consuming and expensive because it consumes a big portion of the project budget for the manpower, tools, and infrastructure used.

How can generative agents help in testing?

Generative agents can help in testing in various ways, such as:

Implementing various types of data such as user profiles, transactions, images, texts, etc. for making the synthetic datasets that goal to remove the necessity of manual data creation and augment the test data accuracy.
Developing mock conditions or cases concerning specifications, requirements, or user stories of the software. It will help in the design of tests and ensure that all the critical concerns and abnormal situations have been examined.
Designing behaviours or actions that basically act like the ones who actually might use the software, customers, or adversaries of the software. This can mime up different types of despairs and cases the program may meet with in the real world to test how functional, performant, secure, and user-friendly the software is.

Benefits of generative agents in software testing

Automation can lead to the elimination of human activities that are boring, repetitive or complex.

During the software testing, programmers often come to do several tasks which are repetitive, self-surfacing, hard, like data generation, test case design, test run, bug discovery, and bug report writing. Such functions, which may take a lot of time, force, and expenses, as well as the introduction of errors and biases, could be either a help or a hindrance. >>Generative agents could do such tasks as creating data or situations, both of which are instrumental in testing by itself, or by talking to the software and analyzing as well as reporting the results. It lessens time, energy, and resources, while also diminishes human mistakes and imperfections.

Providing more Test Coverage and Quality

The additional test coverage and the quality assurance, that this additional coverage provides help ensure that subsequent versions of our application will be stable and free of significant errors. It is the task of production testing to find an adequate test variation and quality so that the software will meet the requisites, specifications and customer satisfaction. Applying appropriate test coverage and good quality can still be difficult, though the software might be highly complex with numerous features or with diverse data, interactions and scenarios. >>Generative agents are one example of the tools which can help in achieving better code coverage and quality assurance, by using more realistic and natural data sets, scenarios, or behavior, which can include all the relevant parameters and edge cases of the software. It improves the software confidence and security as well as discovering the cause-and-effect relationships.

Detecting defects and correcting them was done more rapidly and accurately.

Software testing has to make sure developer get to the errors and bugs quickly and in a stable way so that the software can be perfected within the given deadline and budget. Finding defects promptly and precisely may be a hard task; for example complex or huge mobile applications, some of which are distributed, which have defects that may be few, subtle and intermittent. >>Generative AI can help detection of faults asap and accurately doing so better than a human by the use of natural language to communicate with the software and to analyze and report the results. Increases the quality of the input and output interaction among the testers, developers, and users with a tracking system improvement which also, facilitates the defect resolution and verification.

Challenges and Opportunities

AI agents for software testing are not without their challenges and limitations, such as

Data quality and availability

AI bots’ production depends on the data they feed, which may sometimes be false or incomplete, and also not reflective of reality. Inability of data frequently does not be available when its purpose or meaning stand for confidentiality, security or ethics.
The reliability and quantity of information should be ascertained then used to provide an improved content or information.

Evaluation and Validation

Generative AI agents can come up with new, multi-faceted and original outputs but sometimes these AI systems may also produce outputs which might be of no importance or outright wrong or even harmful.
The outputs have to be examined and confirmed to make sure they reach the targets and goals of the generative assignment, and the outputs do not have any utilization arising to any undesirable issues or incident. One should consider the possibility to involve humans into the process, feedback or supervision could make the drug testing process more complicated and expensive.

Explainability and Transparency

AI agents employ artificial intelligence for the computing complex, and black-box models that end up with outputs that are not easily understood or unboxed by the human eyes.
Consequently, the computational process ought to be articulated clearly and in a way that is totally fair and without bias, especially when the outputs have a lot of impact or implications. This may call for providing the explanations, the justifications or the proofs of the outputs, and also the way for the implementation of the inspection or the modification or the control of the generative process.

On the other hand, AI agents for software testing also offer many opportunities and advantages, such as:

Innovation and creativity

AI systems is a way of coming up with results that the human mind cannot predict or cannot provide solutions, which eventually opens the mind to creative and innovative discoveries.
AI agents, for instance, can generate adversarial inputs and exceptions that can determine how solid and steady the software is or can utilize alternative inputs or creativity that may bring additional functionality to the software.

By exploiting AI agent capabilities and applying them in a way that maximizes benefits for testing, and by actively trying to consistently negate weaknesses of AI agent in this process, we can ensure more productive, clever, and exciting software testing process.

What ways should Generative Agents be implemented to meet the test requirements?

Generative agents are artificial intelligence devices that bring data, cases, or behaviors emulating some policies or targets. They can be used to test software among accomplishing testing tasks, extent of test cases and defects detection being done in an instant. The construction process of the autonomous rovers consist of three phases:

Step 1 : Defining the goal or objective of the generative agent should be the very first action you should take.

The initial formulation focuses on the type of the data when it is scenario or the behavior that the generative agent will produce as well as the criteria or constraints upon which it should follow.

For example, if we choose profiles of users for testing a social media app we need to specify attributes of the user profiles such as a name, age, gender, location as well as preferences. And also the values for each attribute should be within a specific range or in the form of a distribution. Another aspect to think about is the setting of the data quality and quantity standard, for example, the data should be as realistic, as diverse, and as representative as possible, and with what user profiles we have to work with.

Step 2 : Select a method or strategy of acquiring data or creating the reason, situation, or moves.

The second step will be to settle on the way to generate the information, scenario, or behaviour based on the purpose that the generative ultimately agent needs to accomplish.It is important to highlight that different approaches can be taken, which we can mention as rule-based, probabilistic and machine learning-based ones. It is incredibly hard for the various methods with different difficulties and distinctiveness to outrun each other in different tasks.

Methods available and their examples are as per following:

Rule-based approaches take advantage of certain prior rules or logic to generate data, scenarios and behaviors. This is however quick and simple but also regulated and limited.
The mathematical techniques are referred to as probabilistic methods. Those use models based on statistics or maths to derive data, scenes or events which are based on probabilities& distributions. Such techniques are flexible and diverse, but may be uncertain or noisy at the same time.
Machine learning techniques are data or neural network driven models which come up with a data, scenarios or behaviors using a learned pattern or feature from a training dataset and that can be so realistic and creative, however they are also complex and mostly opaque.

Step 3 : Launch and rating the alternating agent.

The Third stage is that it is applying and investigating the generative agent via utilizing the existing environment, frameworks or even by the means of custom code development. The test and validation of a generative model should be done to ensure that it precisely works for the goal or objective, as well as this should ensure the high-quality output of the system and its relevance.

In addition to these three steps, there are some other points that might be beneficial to consider when building generative agents for testing, such as:

Integrate the generative agent with the software under test:

The generative instance must communicate and interact with software under testing at the top level of the algorithm in order to deliver generated the data, test cases or their behavior as the input of software.
This can be attained by the use of APIs, Interfaces, or protocols, which enable communicating between the generative agent and the software by the exchange of the information and the commands.
For instance, assume we are using an agent for generating the user data in-order for us to do the testing of the recommendation system. What that basically means is that we have to integrate the generative agent with the recommendation system. This should allow the generative agent to enable the recommendations that the system can generate and collect the feedback.

Monitor and update the generative agent:

In this context, the generative agent needs to be frequently observed and updated, so as to be able to verify if it’s operating correctly, as well as to make adjustments for the software updates.
This can be achieved by defining thresholds, via instrumentation of logs, metrics or alerts, used to inform about the performance and behavior of generators.
One example of this would be if we have a generative agent which we use to carry out malicious attacks, we need to pay attention and update the agent, to make sure that it only generates attacks that are valid and hence which are relevant and which in-turn are not causing any damage to the security system or its environment.

Developing a Generative Agent: Exploring some situation examples.

To put forth the ways how the generative algorithms could be used during testing, The case of testing a chatbot that is a customer service assistant for an online store. The chatbot would need to be able to reply questions, provide customers with the necessary information and take customers order. To test the chatbot, we can use generative agents to :

Create a mixture of character profiles that looks just like real people, such as names, ages, genders, locations, taste and so on. How the chatbot behaves and react to different people when they come across the chatbot.
Come up with use cases or test cases with attributes like customer profiles and chatbot utilities like asking questions, requesting information, placing orders, etc. This will help pupil to test how chatbot responds to competition situations.
In order to show behaviors and interactions of customers with the chatbot, Should develop such actions like typing a message, clicking a button, providing feedback as well as etc. Like that we can see how our chatbot works in terms of accuracy, data handling speed, reliability and the level of satisfaction.

Generative AI in all the Testing Domains

Generative AI – the newest player on the scene – is reshaping the landscape of software testing across multiple sectors. With the help of fruitful algorithms and machine learning methods, these intelligent systems provide chance to generate a lot of test cases, mimic final user behavior and form a lot of data that more likely corresponds real world instances.

Generative AI is utilized for testing in various environments.

Unit Testing

Generative AI offers the key ability to unit test as it can self-create test cases in line with the requirements and signatures of the functions. This hurries up not only the program’s construction process but also the creation of tests that cover a broad spectrum of input variations.

Integration Testing

Generative AI is the basis for integration testing through its ability to generate interactions between the various internal code elements. It will foretell future failure and develop more targeted testing paths that will add to the effective power of the software thus.

System Testing

System testing can be performed using Generative AI, where the detailed user scenarios for checking the full end-to-end operation of software are created. Moreover, it can foresee the behavior of the system under different conditions so that editors may identify areas of more bugs.

Performance Testing

Generative AI features facilitate performance testing by representing the target user personas and traffic load behaviour. This checking tool helps to pinpoint the general system capacity of the system and locate the spots of the resource deficit.

Security Testing

Security testing with Generative AI creates a variety of attacks and malign data which are used for checking the security of software concerning the system of threats. Another way it can do that is by learning from past security vulnerabilities to predict new and unexplored ones.

Technologies Available

There are many technologies available for building generative agents for testing, such as:

1. Rule-based systems – It can utilize predefined conditions, facts or methods to perform data generation, simulation or behavior projection. As an illustration, Mockaroo is a mocking-up tool that can create patterned datasets, which either are random or as per the defined rule and scheme by a user.

2. Stochastic models – Takes probability and distribution-based models to give data, scenarios or behavior under probabilistic or distributional modelling purpose. Like Faker, the Python library can provide such fakes as names, addresses, and dates based on a lot of places and their formats.

3. Machine learning models – Data-driven models or models using neural networks’ learning features or patterns from training data’s to produce data or scenarios or behaviours. To illustrate, GTP-3 is a deep learning model that can imitate human writing and produce natural language text when given a prompt or context.

Use cases

Some of the use cases of generative agents/technologies for software testing are:

Image generation and manipulation

This will be a scenario of generating pictures or manipulating them for testing purposes; for example generation of pictures for checking the ability of the computer vision system and manipulating images for testing the performance of an image processing system.
For example, the StyleGAN2 is a machine model that can generate realistic and different images of/with the face, the animal and the landscape, etc. in the style of/with the given style or attribute.

Software and coding

Developing and modifying source codes for testing tasks like creating the code blocks for testing the behavior of programming languages or simply modifying some existing codes with the intention of exploring software vulnerabilities.
CodeGPT, for example is a machine learning model that offers autocompletion of codes either in a certain programming language or a particular task.

Video creation

Implementation during video processing like video analysis system testing (generating synthetic video sequences) or video editing system testing (editing video for testing).
Deepfake video anime, for instance, is a machine learning model that renders a video based on a given image or video.

Automated Test Case Creation

AI generative tool, by getting a grasp on the demands and features of software, can automatically generate a variety of test cases and therefore make sure full coverage, even the conjunctives missed usually in the manual process are included.

Performance Benchmarking

Through Generative AI, it is possible to create an environment where software is put under different stress conditions in order to gauge how the software and system perform under the duress and yield insights on scalability and stability.

Security Penetration Testing

AI-stimulated agents smart enough to detect Threats using different routes can simulate many scenarios uncovering existing computer system security shortages.

Practical use case: GitHub Copilot for Unit Testing, a code-generating AI tool that can help developers test their code more efficiently and effectively.

In the realm of program testing, GitHub Copilot works as an AI pair programmer with a capacity of generating suggestions that in turn aid in the creation of test cases that can be implemented at a relatively lower cost compared to traditional methods. This tool largely serves for unit testing of a particular function, where it can suggest tests that are predicted based on function behaviour and expected outcome. Developers could use Copilot to rapidly facilitate the creation of a number of test cases in the active areas, thus promoting the overall test quality.

GitHub Copilot to suggest relevant unit tests for a new feature the developer is currently working on.
Copilot studies the code and offers suggestions of tests that involve scenarios covering normal usage, edge cases and exception handling.
This will consequently enhance the test development process and allows in a better way to keep high code standards.

GitHub Copilot in Action Across Testing Types:

Unit Testing: The capability of GitHub Copilot can go to the extend of entire function blocks suggestion of unit tests based on signatures and the logic within your code thus making test coverage thorough.

Integration Testing: Through the knowledge of how sections of code invoke each other enhancements in the testing of integrations that insure components work together smoothly can be brought into reality.

End-to-End Testing: In order to do full-fledged system tests, use GitHub Copilot to write test scenarios that cover the whole scope of the system, analysts will develop the scenarios that can simulate real-world user behaviour and interaction with the application.

Performance Testing: GitHub Copilot will help in script performance test as he would use code which is just like heavy traffic conditions and user load.

Security Testing: Once security comes to GitHub Copilot’s arm, then it can build tests to find the weaknesses and possible exploits in the extent of the code.

Conclusion

Generators open mind-boggling possibilities and give directions in software system testing. They can also provide on-demand object generation, multiply scenarios or actions which could lead to improvements in product quality, testing, release, and overall user experience. They can even expand testers’ reach and incorporate new possibilities and challenges for performing tests such as reproducing creative and adversarial input data. This may increase the efficiency and robustness of the software. Generative agents do not replace human testers, but rather augment them. They increase their agency by making them market leaders in their respective industries.

Refrences

1.Generative artificial intelligence

https://en.wikipedia.org/wiki/Generative_artificial_intelligence#

2.Generative AI Use Cases

https://aws.amazon.com/ai/generative-ai/use-cases/

3.AI for Good Blog