Hello, everyone. A warm welcome to Conf42, Incident Management 2024. Thank you all for joining me today for my presentation. My name is Prakash Oja, and the topic of my presentation today is AI Driven Code Review and Quality Assurance, Revolutionizing Software Development, A New Era in Software Engineering, Efficiency and Quality. AI really has taken the world by storm over the last few years. It has become an inescapable part of the software development landscape, including code review. you cannot just avoid hearing about it. AI offers unique opportunities to revolutionize quality assurance, ushering in an era of higher efficiency and quality. presentation today is divided into the following sections. About the paradigm shift that's happening in software development due to AI. Why do we need AI in code review? The key benefits of AI driven code review. How is AI transforming testing with its presence in quality assurance? How do we quantify the AI revolution? What are the metrics for success? And then we'll move on to real world application of AI driven code review. What are the challenges and considerations with AI driven code review? The future of AI driven software development. And finally, I'll be offering my concluding remarks. paradigm shift in software development The software development landscape is undergoing a profound, a significant transformation due to the rapid advancement in artificial intelligence and machine learning technologies. Every day, you have new AI tools coming out and they are each more advanced than the others. Traditionally, developers have relied on manual code reviews and human driven quality assurance processes. Which, while effective, are slow due to their very nature. They are prone to errors and they are very resource intensive. AI driven tools are now enabling software development teams to automate these processes. AI tools can analyze vast amounts of code in real time. They can detect patterns that would likely go unnoticed by human reviewers. and they provide immediate feedback during the development itself. This shift towards AI powered development is not only enhancing the accuracy and speed of code reviews, but also revolutionizing how software is developed, tested, and maintained, paving the way for higher productivity. And a better quality products. need for AI in code review. The challenges of manual code review. Traditional code review processes rely on developers manually checking each other's code for issues like bugs. performance bottlenecks and security vulnerabilities. This method is effective in smaller codebase, but as the codebase grows larger and larger, it becomes inefficient. AI as a solution. There are tools such as Amazon CodeGuru and DeepCode. That lever is advanced algorithms to automate this process of reviewing the code allowing for faster and more accurate code analysis. These tools can scan through thousands of lines of codes in minutes, identifying issues such as memory leaks, inefficient algorithms, improper error handling, and security vulnerabilities with a very high level of precision. What are the impacts of AI tools in software development? The AI driven code reviews reduce the time by 75%, up to 75%. this allows for faster iteration of development, quicker delivery of features, new features, and quicker delivery, for bug fixes. And not only do these tools catch common errors, But they can also learn from the past reviews, improving their ability to detect complex issues over time. So as the time goes, they get better and better. They improve further. What are the key benefits of AI driven code review? First of all, enhanced code quality. AI tools continuously learn from fast code repositories, identifying potential problems, and with greater accuracy than traditional methods. They improve the development lifecycle, making them faster. By automating reviews, AI allows teams to iterate faster while maintaining a high quality without compromising on the quality. AI, can also offer predictive analytics. AI uses historical data to predict potential bottlenecks and bugs before they can happen. This allows developers to be proactive. How is AI, in quality assurance transforming testing? AI automates test case generation, prioritizes tests based on recent code changes, and it enables self healing tests that adapt to changes in UI or code structure. AI can also analyze historical test data to predict areas of the code most likely to contain bugs, allowing QA teams to focus their effort more efficiently. offers enhanced test code coverage. AI driven testing tools automatically generate diverse sets of test cases and they ensure that edge cases and rare scenarios are adequately covered, which can get missed by a human reviewer. This leads to more, more comprehensive testing and higher overall code reliability. Traditional testing methods focus on predefined cases, which means they can miss potential bugs that can arise from, uncommon user behaviors or interactions. How do we quantify the AI revolution? How do we measure the success, the matrices? first of all, the bug detection rate. AI driven tools improve bug detection rate by 35. 7%. Identifying subtle issues that manual reviews often miss. Development cycle time. AI reduces development cycles by 33. 3 by one third. This allows teams to shift features faster. Code review time. AI automates review process, cutting review times by 75%. Test case generation time. Automated testing powered by AI reduces the time spent on test case generation by 75%. About by three quarters of time leading to quicker iterations and better test coverage. And finally, develop developer productivity. Developers can write up to 50 percent more lines of code per day with AI support. They can now focus on more complex and more value added tasks. What are the real world applications of AI driven code review? Real world applications of AI driven tools like Amazon's CodeGuru have significantly impacted organizations by improving the quality of code and reducing system downtime. For example, Amazon CodeGuru alone has identified, it has been found to have identified 90 percent of the critical issues in the code. leading to a 50 percent reduction in downtime. These AI tools can scale across large projects, helping organizations ensure code consistency and security. with AI tools, we also have some challenges and some considerations, primarily adoption barriers. In order to adopt AI tools, teams must invest in training and infrastructure to effectively integrate AI driven tools into existing development environment. And this is not easy. Tool integration. The ability of AI tools to learn from a unique code base requires continuous improvement and maintenance. This requires, time and effort. Continuous learning. AI tools need constant training and updates as they encounter new coding patterns, ensuring their long term effectiveness. The future of AI driven software development. As AI evolves, it will become increasingly embedded in every aspect of the software development lifecycle. including CICD, productive maintenance, and automated cost standardization. AI driven personalized developer environments and tools enable faster, more creative software development while maintaining very high standard of quality and security. Future advancement will continue to reduce development cycle times, increase code quality, and enable faster time to market. Finally, the concluding remarks. Artificial intelligence is not just enhancing software development processes, it's transforming them. The ability of AI driven tools to accelerate development cycles, enhance code quality, and improve overall productivity is already making waves across the software industry. AI powered code review and quality assurance processes are bringing unprecedented levels of efficiency and accuracy to teams. Allowing the developers to focus on higher value, creative problem solving tasks as more and more mundane tasks is being delegated to AI. As AI continues to evolve and mature, it will become an essential part of the software development lifecycle, delivering smarter, faster, and more reliable software. Organizations that adopt AI driven tools will not only improve their development workflows, But also gain a competitive advantage by delivering higher quality products. you very much for joining me today.