Today is just the beginning of Generative AI and its countless benefits. Generative AI applications such as ChatGPT, GitHub Copilot, DeepArt, and others have taken the world by storm by unleashing a wave of innovation, creativity, and productivity. We are now able to do tasks that were previously unimaginable.
Generative AI – Catalyst or Replacement?
Generative AI can mimic human capabilities to an astonishing level, raising fears about AI replacing humans. However, it’s important to understand that Generative AI will only empower humans and not replace them.
Leaders across organizations are realizing that they can actually unlock exceptional accomplishments by nurturing this collaboration between humans and Generative AI.
So what are the potential business benefits?
Generative AI is being used to create new and unique content, spanning across various domains like art, music, graphics, social media, and beyond. Let us explore some of the business benefits it can offer when used in the software engineering disciplines.
Generative AI Use Cases
Let’s consider a scenario where a software application is being developed for a banking platform and needs to be tested. Now, let’s see how Generative AI can help us reduce manual effort, accelerate testing cycles, and help us improve efficiency across the following use cases.(Generative AI in software testing)
Test Case Generation
Generative AI can help in generating comprehensive test cases by analyzing the requirements and existing code of the application. You can also use it to cover positive and negative test cases and edge cases.
For the banking application example, it can generate test cases to cover various scenarios such as creating new accounts, making fund transfers between different account types, testing different transaction limits, and verifying balance.
Code Completion
Imagine the amount of time developers can save if they could get recommendations and code snippets to expedite the coding process. Generative AI understands the semantics and structure of the existing code and thus provides accurate suggestions.
Examples include CodeAI and GitHub’s CoPilot. They use contextual understanding, trained models, and code repositories to generate code snippets, accelerating the coding process and aiding the developers in reducing human errors.
According to Mckinsey research, technology companies are already using Generative AI such as Replit which is being used by more than 20 million coders today.
Test Data Generation
Thorough application testing requires a diverse range of test data to simulate different scenarios and ensure overall test coverage. Generative AI can help create realistic test data.
For the banking application scenario, it can generate sample customer account details, names, addresses, and other required test data.
Code Optimization
The development teams work on applications that involve complex code and algorithms. Generative AI algorithms can analyze code and identify any performance bottlenecks. They can also propose code refactoring suggestions to enhance readability.
For example, it can identify inefficient algorithms or resources used in any feature that takes up the maximum CPU usage, and suggest alternative implementations that improve the execution speed. This simplification reduces the complexity of the code, making it easier to test and maintain.
Defect Identification and Prediction
With Generative AI, it’s easier to identify possible coding errors or any deviations from the coding standards. By leveraging AI and machine learning techniques, it can learn from previous defects, detect patterns and also predict defects.
For example, Webomates’ AI Defect Predictor helps the development and QA teams that use CI/CD service to reduce their triage time. This proactive approach enables the development and testing teams to correct their code and reduces the overall effort required in automation testing.
Generate Documentation
By extracting data from code, test cases, and other resources, generative AI can automate the documentation process. Developers and testers don’t have to spend their time manually updating and creating documentation, which saves them time and effort.
Accenture is testing the use of OpenAI LLMs to automatically generate documentation – for example, SAP configuration rationale and functional or technical requirements.
Limitations of Generative AI Capabilities
Although the software development and testing teams are opening up to using Generative AI in software testing, it still comes with a set of unique challenges and limitations.
How Can Webomates Help?
Generative AI is here. And as it keeps evolving, it will unlock new possibilities for improved efficiency and innovations. Meanwhile, we can leverage it to transform our application testing and automation efforts.
Webomates understands that AI-based software testing speeds up product releases and generates the promised business value. It’s an industry- and tool-agnostic solution to optimize testing in various scenarios.
Schedule a demo to discover the full potential of our AI-driven software testing solutions that can boost your business outcomes, increase productivity, and accelerate product releases. You can also reach out to us at [email protected]
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Not all heroes wear capes, well, as far as I know, Artificial Intelligence can't!
AI has the power to tackle some of the world's social problems too (the real heroes are the masterminds who thought of AI in the first place).
For example: helping the blind by directing them, diagnosing cancer and many many more.
People are just learning to use AI and the possibilities are vast.
Which is why interest in AI has dramatically increased in the past few years.
And Google has taken this social initiative naming it “AI for Social Good”First of all, what is AI?
In 2016, the calculable worth additional by the agriculture business was calculable at but one percent people GDP.
Factors like global climate change, increase and food security considerations have pushed the business to hunt a lot of innovative approaches to safeguard and improve crop yields.
As a result, AI is bit by bit evolving as a part of the technology evolution of the business.In this article we have a tendency to explore the applications of computer science to know current and rising trends for business leaders, and gift representative samples of common applications.Artificial Intelligence within the Agricultural business - Insights Before:Based on our analysis, the foremost common applications of AI in agriculture make up 3 main categories:Agricultural robots: - corporation’s area unit developing autonomous robots to perform the required agricultural tasks, like harvest crops and fast quicker than human labor.
Crop and Soil observation: - corporation’s area unit developing laptop vision and deep learning algorithms to method knowledge compiled by drones and / or software-based technology to watch crop and soil health.Predictive Analytics: - Machine learning models area unit being developed to find out and predict numerous environmental impacts on crop yields like global climate change.Blue watercourse Technology - Weed management:The ability to regulate weeds may be a high priority for farmers associate degreed an in progress challenge as weed killer resistance becomes a lot of common.
per a quest study conducted by the Weed Science Society of America on the impact of uncontrolled weeds on corn and soybean crops, annual losses to farmers area unit calculable at $ forty three billion.
Companies’ area unit exploitation automation and artificial intelligence to assist farmers realize a lot of economical ways in which to safeguard their crops from weeds.
Doing so gives the company a competitive advantage while improving marketing and advertising performance.With that said, today I share the emerging trends in the AI industry, and what you need to know about moving into 2020.Also Read: Top 10 AI Trends Marketers Should Watch for In 2020Predictive AnalyticsYou don’t need a crystal ball to know the future.
Using analytics, a company can use models and trends to improve everything from its advertising to security.Not only is it more widely used, but it can also help businesses increase their bottom line while taking advantage of competitors, thanks to:The easy barrier to entry with easy to use and affordable platforms.2.
This is a 21% compound growth from 2016 and seems to be trending toward that, making it a worthwhile AI trend to keep your radar on.Higher Use Of Anomaly DetectionMissing budgets, breakdowns of integrations, and forgetting to start are some of the daily woes the agency faces.
These are all human failings, and also completely normal.
Ultimately, it allows agencies to focus on the things that humans do best, while AI takes care of optimizations in the background.Machine Learning-Driven CybersecurityCybersecurity is a growing concern worldwide.
In fact, 67% of small businesses will experience cyberattacks in 2018.
