Table of Content
Understanding Generative AI
Generative AI represents an advanced stage in the evolution of artificial intelligence, built upon several layers of development. These layers include:
- Artificial Intelligence (AI): The foundation, enabling machines to mimic human intelligence.
- Machine Learning (ML): A subset of AI where systems learn from data to improve their performance.
- Deep Learning: A more complex layer of ML, utilizing neural networks with many layers to analyze vast amounts of data.
- Large Language Models (LLMs): Advanced deep learning models that understand and generate human-like text based on extensive datasets.
- Generative AI (GenAI): The latest evolution, leveraging all previous layers to analyze existing data and generate new, original data.
What is Gen AI?
Generative AI leverages machine learning and deep learning to analyze existing data and generate new, original data. This advanced technology marks a significant evolution in AI, moving beyond mere data processing to creating novel outputs based on learned patterns and insights. Furthermore, GenAI can imitate human domain experts and analyze data through the lens of these experts, providing very advanced reasoning in its answers and ensuring high-quality output.
How it works?
Traditional artificial intelligence focused on analyzing and mimicking data, identifying objects, summarizing information, and generating outputs based on existing data.
Generative AI, however, represents a significant shift. It takes vast amounts of input data within a specific context and uses this to generate entirely new data that did not exist before.
This capability makes Generative AI incredibly versatile. It acts like an expert with extensive knowledge on a given topic, capable of not only summarizing data but also analyzing it to produce additional, contextually relevant information. This is the true power of Generative AI – creating new insights based on the data provided.
Benefits of Generative AI in Business
Enhancing Efficiency with AI
Business units traditionally rely on various domain experts, such as data analysts, testers, and coders, to process data and generate results.
Generative AI accelerates processes by acting as a domain expert, providing high-quality business solutions and automating tasks previously managed by specialists. It alleviates bottlenecks by taking over simple, repetitive tasks, enabling businesses to replace human effort with efficient automation.
Cost Savings with Generative AI
Generative AI accelerates numerous business processes by automating daily micro tasks typically managed by employees. Companies that recognize and harness the potential of Gen AI can significantly increase their margins. This is primarily due to the reduction in the workforce needed, as employee salaries are often the highest expense in any business. The Return on Efficiency (ROE) can be measured by the reduction in labor costs and the increased productivity from streamlined operations.
Generative AI in Software Development
How do you accelerate development cycles with AI?
Accelerating development cycles with AI involves using large language models (LLMs) to analyze lots of code. These models are trained with many parameters and huge codebases, enabling them to understand coding patterns and structures.
Code has been around for a long time, with many examples and best practices. Many developers do similar tasks with consistent processes, all governed by code, which is structured and rule-based, like any other language. This structure makes it possible for AI to learn from existing code and generate new code, processes, and solutions.
By using Generative AI, developers can:
- Automate Repetitive Tasks: AI can handle routine coding tasks, freeing developers to focus on more complex and creative work.
- Improve Code Quality: AI can spot potential errors, suggest improvements, and ensure best practices, leading to better code.
- Enhance Productivity: With AI generating standard code and templates, developers can save time on writing basic functions and components.
- Facilitate Learning: AI can quickly adapt to new programming languages and help developers learn new technologies.
- Innovate Faster: By automating parts of the development process, teams can create new features and products more quickly.
AI-Driven Code Generation
One of the biggest challenges with large language models (LLMs) is the context size. How much data can you feed into the short memory? The amount of code and complexity required to describe a business application is usually extensive. Therefore, it is impossible to simply instruct the AI to generate a new app based on features you have used previously.
This makes Generative AI a copilot rather than a pilot. It speeds up development by assisting rather than completely taking over the process. This is particularly beneficial for solo startups. If you have domain expertise, enterprise software development can be accelerated by using Generative AI as a copilot.
This allows businesses to generate hundreds of thousands of lines of code. To ensure this process runs smoothly, managers conduct peer reviews to ensure everything is correctly orchestrated.
Enhancing Collaboration and Innovation in Development Teams
Imagine an extra team member, who is constantly there and sees all the code – that is Gen AI. It can analyze code and see when team members do not follow the approved structure, which allows you to correct the issue quickly. Gen AI is a team member that provides accurate feedback. Moreover, it can stop you from working incorrectly, suggesting a fix before a task is submitted.
Another example of Gen AI as a copilot relates to collaboration. All team members can see what everyone is doing, without needing to sift through the code files generated daily. This means it can suggest a specific component is reused, as it has already been peer reviewed by two people within the organization. Reusability is a huge advantage alongside collaboration.
However, development teams are not just about code; they often involve collaboration and input from many stakeholders, including product managers, project managers, coders, and testers. At Panaya, we fully embrace Generative AI and envision a future where collaboration and business process development are seamless. Using our platform, we gain full transparency into the connections between requirements, code development, testing (both manual and automated), and the overall project overview. This holistic view is crucial for efficiently managing the business application development process.
- Unified Overview: Panaya provides a unified overview of the entire development lifecycle. From initial requirements to final deployment, every aspect of the project is visible and interconnected. This ensures that all stakeholders have a clear understanding of the project’s progress and can make informed decisions based on real-time data.
- Seamless Integration of Requirements and Code Development: With Panaya, requirements are directly linked to the corresponding code development activities. This integration ensures that developers are always aware of the specific requirements they are addressing, reducing the risk of misalignment and ensuring that the final product meets business needs.
- Comprehensive Testing Transparency: Panaya bridges the gap between manual and automated testing. All test cases, whether manual or automated, are connected to the relevant code and requirements. This comprehensive view allows testers and developers to see exactly how changes in the code affect the tests, ensuring that quality is maintained throughout the development process.
- Real-Time Notifications and Updates: To facilitate effective communication, Panaya offers real-time notifications and updates. When changes are made to requirements, code, or tests, relevant team members are immediately notified. This proactive communication ensures that everyone is on the same page and can respond quickly to any issues that arise.
- Detailed Project Overview: Panaya’s dashboard provides a detailed overview of the entire project. Key metrics, progress reports, and status updates are all available at a glance. This high-level view is essential for project managers to monitor timelines, resource allocation, and overall project health.
- Ensuring Accountability and Traceability: Every action in Panaya is tracked, ensuring full accountability and traceability. Team members can see who made changes, when they were made, and why. This level of detail is crucial for maintaining high standards of quality and for auditing purposes.
- Seemore Co-pilot: Panaya utilizes its unique end-to-end context and, with the help of Generative AI and conversational interface, enhances collaboration by providing proactive and reactive insights. Team members can easily collaborate on requirements, code reviews, and test results, ensuring that all perspectives are considered and integrated into the final product. It’s like having a conversation with the business process itself.
A secondary advantage of collaboration with Gen AI is it allows team members to ask questions without feeling foolish. There is no judgement, it simply provides an answer, allowing team members to continue with their work with complete confidence.
Gen AI in Software testing
Automating Testing Processes with AI
Manual Testing
Test automation is a complex field, and not everything can be automated. Smart testing recognizes the need for manual intervention, especially when it’s unclear how to move from manual to automated tests. At Panaya, we know that the process starts with manual tests. These tests give us the essential context and understanding needed for successful automation.
Just like ChatGPT was trained using human feedback to refine its responses, we use feedback from many users to improve our GenAI. ChatGPT learned to reason and provide better answers through continuous feedback from people, which is a form of crowd wisdom. Similarly, at Panaya, we leverage feedback at scale to teach our GenAI. This crowd wisdom helps our GenAI make accurate suggestions and improve over time.
With our experience of managing and running over 10 million tests, we use GenAI to suggest which tests are best for automation. Our approach is based on the project details and input from everyone involved. By analyzing manual tests, we help you move smoothly to automated testing. We understand product flows and their impacts, so we can make sure the right tests candidates are used at every step, making the process more efficient and effective.
Test Automation
Test automation has long been essential for ensuring that software applications run smoothly and meet their requirements. However, the traditional approach to test automation faces significant hurdles. It often involves mapping every action to mimic user clicks, which can be time-consuming and labor-intensive. Moreover, when changes are made by vendors, these automated test flows can break, requiring extensive fixes. Generative AI (GenAI) is set to revolutionize this process. With its multimodal capabilities, GenAI can see and analyze images, read code, and reason about changes, significantly speeding up the process and lowering the entry barrier for creating and operating test automation flows.
Panaya is committed to build the next Generation solution for Test Automation, by focusing on the following:
- Tailored AI Automation Engine: Optimized for SAP, Salesforce, and other cloud applications, creating resilient scripts that adapt to UI changes with minimal maintenance.
- AI Simplified Automation: Utilize natural language processing to automate complex tasks effortlessly, generating high-quality test data and enhancing test coverage.
- AI Code Assistant: Automatically generate and explain code snippets, simplifying complex expressions and boosting efficiency.
Panaya’s AI Co-Pilot, SeeMore, offers real-time project insights, expert guidance, personalized next steps, and proactive assistance, ensuring you navigate the complexities of business application testing with ease and efficiency.
Challenges and Solutions in AI Implementation
One of the first challenges is compliance if you are using proprietary data. You must understand which infrastructure you are using to select the correct one and ensure it is segregated. It must be compliant based on your company policy.
Cloud vendors are aware of this issue and provide services that allow you to have enterprise ready LLMs, with an infrastructure that is built for enterprises. Selecting which vendor, you work with is the first choice, which must be based on infrastructure.
The second issue is cost. AI operates using tokens (units of data), so if you input a large amount of text, you will receive a lot of text in return, which can be expensive as you pay for each token. Therefore, it is important to implement safeguards to prevent rapid depletion of resources. Additionally, understanding user engagement is crucial—extensive use of AI by users can lead to high costs.
Consider the specific use cases. Different LLMs excel in different areas. Keep in mind that different models are optimized for different tasks: some are better at summarizing, others excel at coding, and some are superior at reasoning within specific domains.
Train your organization in using this new technology wisely. Interacting with Generative AI and LLMs is different, and without proper training, people may misuse it and only gain a fraction of its potential value. If not set up correctly, the results will be garbage in, garbage out. Proper setup and employee training are crucial for harnessing the full potential of this technology. With great power comes great responsibility; we need to refine and use this power wisely to achieve the promised value and significantly enhance productivity and outcomes.
Who Are The Top Gainers From Gen AI?
There are three types of companies that stand to gain the most from GenAI technology:
- Infrastructure Providers: These are the companies that build and maintain servers, such as Nvidia. They provide the essential hardware that supports AI operations.
- Service Providers: Companies like Amazon, Google, and Facebook offer the large language models (LLMs) needed to create GenAI use cases. This technology is expensive and requires vast amounts of data to function effectively.
- Data Curators and Domain Experts: This is where Panaya fits in. Companies with specific domain data, like Panaya with its in depth understanding of packaged application business processes and the world of smart testing that surrounds it, have the most to gain. These companies can leverage their extensive data and domain expertise to provide valuable services.
For Panaya, this means using domain-specific data about testing to offer tailored services. Companies prefer using our tools and services over starting from scratch. Panaya provides the ability to generate specific tests for specific business flows based on the collective experience of hundreds of users who have previously created similar tests. Furthermore, we offer these suggestions in multiple languages, thanks to the multilingual capabilities of LLMs.
Unlock the full potential of Generative AI with Panaya. Our vast experience enables customers to maximize the benefits of this technology using their own data. With Panaya, you can ask any question and receive answers tailored to your specific needs, leveraging our domain crafted context and data to add significant value.
Panaya enhances smart testing, whether manual or automated, by connecting all the dots utilizing the domain expertise gained from thousands of companies and millions of tests. Our intuitive, GenAI-focused product delivers this expertise directly to your fingertips, ensuring seamless and efficient testing processes. Join us to revolutionize your testing journey with unparalleled precision and insight.
