The advent of Artificial Intelligence(AI) as a science gave birth to many possibilities and of course, it allowed our movie directors to come out of their creative block to make some good Sci-fi movies based on AI like Chappie, iRobot, Eagle Eye, etc. What is more exciting is the revenues that AI will fetch as it continues to aid various industries to be more efficient and productive. A recent report by McKinsey estimates that Generative AI will add a whopping $2.6trillion to 4.4trillion across 63 use cases in the years to come. And with the advent of the new economies comes growth and change. A change to make our efforts easy, efficient and effective.
What is Generative Artificial Intelligence (Gen AI)?
Generative AI has taken center stage in recent times with the introduction of Chat GPT and Dall-E. Simply put Generative AI is your modern-day genie from a bottle that creates different types of content like – text, images, and audio. While Generative AI might seem like a recent idea, the birthing and evolution of Generative AI takes us back to the 1960s when Joseph Weizenbaum, a German computer scientist and Professor at Massachusetts Institute of Technology developed a program called ELIZA. An intelligent machine that tricked its users into believing that they were conversing with another human, similar to the modern-day chatbots. It was Alan Turing who was way ahead of time and thought of these technological marvels before the fancy tech of today existed.
History of Generative AI
The roots of Generative AI can be traced back to the concept of machine learning. Back in the late 1950s, scientists and researchers were already exploring the idea of using algorithms to create new data. One of the early examples of Generative AI was the Markov Chain, which could generate new sequences of data based on input.
However, the real breakthroughs happened in the 1990s and 2000s when advanced hardware and digital data became more accessible, allowing machine learning to shine. The birth of Generative AI, as we know it today, was marked by the emergence of neural networks. These models, inspired by the human brain, use interconnected layers of “neurons” to process and learn from data. Once trained, a neural network can make decisions or predictions without explicit programming.
The real creative power of Generative AI comes from a specific type of neural network called Generative Adversarial Network (GAN), which was introduced by Ian Goodfellow and colleagues in 2014. Alongside GANs, other generative models like Variational Autoencoders (VAEs) and Recurrent Neural Networks (RNNs) also gained traction, showcasing their ability to generate novel content. These advancements set the stage for the growth of Generative AI and the development of more sophisticated models.
The last decade has given us a lot of advancement in the AI space, we’ve witnessed several pivotal moments that have reshaped the landscape of Generative AI –
- WaveNet (2016): DeepMind’s WaveNet enabled realistic human speech generation, leading to more human-like AI assistants and accurate text-to-speech synthesis.
- Progressive GANs (2017): The introduction of Progressive GANs by NVIDIA impressed us greatly as they produced high-resolution, photo-realistic images with incredible detail.
- GPT-2 and GPT-3 (2019, 2020): OpenAI’s GPT models were game-changers for text generation, showcasing the ability to generate coherent and contextually relevant sentences, making them useful for various applications like writing assistance and chatbots.
- DALL-E (2022): Witnessing the launch of DALL-E by OpenAI, a model capable of generating digital images from natural language prompts, expanded the boundaries of creativity.
- ChatGPT (2022): OpenAI’s release of ChatGPT, a conversational chatbot based on GPT, left a lasting impact as it quickly gained popularity with one million users within five days.
- GPT-4 (2023): The arrival of the latest GPT model, GPT-4, with improved accuracy and advanced reasoning capabilities, was a significant advancement. Moreover, as premium ChatGPT users, we gained optional access to GPT-4 within the chatbot.
Each of these milestones in Generative AI has been instrumental in overcoming challenges related to computational power, data quality, and training stability, bringing us closer to the impressive capabilities we have today.
Generative AI in App Testing
I’m sure you’re wondering what ChatGPT or Dall-E have to do with App Testing? Or How will Generative AI help you perform your testing tasks easier and faster? To answer that, we need to delve a little deeper into the functions of App Testing and see where AI can take the load off. There is no blanket approach to testing. Testing a particular app involves various stakeholders, various teams with different requirements. Now where and how generative AI will fill in the shoes of different functions is for us to figure out.
Using Generative AI in Testing isn’t as easy as writing your requirements on ChatGPT and voila you have an automation script to run. While some do that, it isn’t effective, since you will be doing a lot of manual tasks of copying the code and customizing it for it to be execution worthy. With that said. Here is a brief overview of the use of Generative AI in Testing.
1.Bug Detection and Feedback
Identifying and locating bugs is a crucial part of app testing. Generative AI can analyze the app’s responses and behavior, comparing them against expected outputs. By doing so, it can quickly detect anomalies and potential issues in the app, enabling faster debugging and resolution. These cutting-edge techniques involve the analysis of vast amounts of application-related data, including code and logs, to identify patterns and deviations from expected application behavior. By identifying these anomalies, generative AI models can effectively pinpoint potential bugs, vulnerabilities, and performance bottlenecks at an early stage in the development process.
Tech giants such as Facebook and Google have successfully utilized generative AI techniques to improve bug detection and expand test coverage. Google’s DeepMind has employed generative AI models to optimize and elevate the testing process for their machine learning systems, resulting in the creation of more robust and reliable models. Similarly, Facebook’s Infer, a static analyzer used for identifying bugs in mobile applications, harnesses generative AI to detect complex coding issues and vulnerabilities, leading to enhanced quality.
2. Enhancing Test Coverage
Generative AI plays a crucial role in enhancing software quality assurance by significantly improving test coverage. By harnessing advanced algorithms and extensive datasets, generative AI models have the capability to automatically create comprehensive test cases that encompass a wide range of scenarios and inputs. This automated test case generation not only reduces the effort and time required but also enhances the overall effectiveness of the testing process.
For instance, consider a web application that necessitates testing across various browsers, platforms, and devices. Generative AI can create test cases covering multiple combinations of these elements, ensuring thorough coverage without the need for extensive manual setup and test case development. As a result, testing becomes more efficient, enabling quicker detection of bugs and instilling greater confidence in the software’s overall quality.
Future Opportunities of Generative AI in Testing
Generative AI holds immense potential to reshape the testing landscape and elevate the overall assurance of app quality. As the field of artificial intelligence continues to advance and evolve, generative AI techniques are surely going to play a pivotal role in driving innovations in app testing. Here are some key areas where generative AI can bring about significant advancements:
1.Test Data Generation
One of the critical challenges in app testing is obtaining a diverse and realistic dataset to simulate various scenarios and user interactions. Generative AI comes to the rescue by creating synthetic test data that closely resembles real-world inputs. This allows testers to assess the app’s performance under different conditions, ensuring thorough and comprehensive testing.
2. Automated Test Case Generation
Writing test cases manually can be time-consuming and error-prone. Generative AI can automate test case generation based on the app’s specifications, requirements, and potential use cases. By understanding the app’s functionalities, Generative AI can quickly generate test cases with broad coverage, saving time and effort for the testing team.
3. Exploratory Testing
Traditional testing approaches may miss certain edge cases and scenarios. Generative AI can be employed for exploratory testing, where it autonomously explores the app and tests it in ways that human testers might not have considered. This can uncover new issues and edge cases, providing a comprehensive testing approach.
4. Regression Testing
As apps evolve and receive updates, regression testing is crucial to ensure new changes don’t break existing functionality. Generative AI can automate regression testing by generating new test cases that cover changes made to the app. This ensures that updates are thoroughly tested, reducing the risk of introducing new bugs.
5. Performance Testing
Performance testing is essential to ensure an app can handle user loads and usage spikes. Generative AI can generate load and stress tests, simulating different user traffic scenarios to assess the app’s performance. This helps identify bottlenecks and potential performance issues early in the development cycle.
6. Usability Testing
User experience is a key factor in app success. Generative AI can simulate user interactions and behavior patterns to evaluate the app’s user interface and overall usability. By analyzing user feedback and actions, it can help identify potential usability issues, ensuring a smoother user experience.
7. Consumer Behaviour
Generative AI can be a game-changer in cases where improving accuracy or user journeys is a major priority. Gen AI can significantly enhance the testing and refinement of systems by generating synthetic user-profiles and simulating diverse purchasing behaviors. This approach allows for more comprehensive and in-depth testing, leading to an improved and more in-depth insight into consumer behaviors.
8. Visual Testing
Generative AI is already creating ripples in the Visual Testing space, as it can be trained in various image recognition and computer vision applications. Generative AI can be harnessed to automate visual testing, a method used to assess the appearance of an App UI or website. This automation helps in verifying that the visual elements of the UI or websites align with design requirements and ensure its overall correctness in terms of appearance. While there are many businesses that are already leveraging AI to accelerate their visual testing efforts. There is still a lot of scope for improvement and enhancements.
Generative AI is transforming the testing landscape for the better. The significant offers and advantages it brings to quality assurance is unmatched. Of the many benefits it offers improving test coverage by automatically generating comprehensive test cases, covering a wide range of scenarios and inputs is no doubt a favorite among the testing community. Faster identification of bugs is no doubt a game changer, thanks to its ability to analyze large volumes of test data and recognize irregularities in application behavior. Overall, generative AI proves to be a valuable tool in enhancing the app testing processes and elevating the overall quality of mobile apps.