Hello, everyone. A warm welcome to CON42 JavaScript 2024. Thank you for joining me today for my presentation. I'm honored to be speaking here. My name is Prakash Oja, and the topic of my presentation today is AI driven code review and quality assurance revolutionizing software development. In the last few years, AI capabilities have grown rapidly and they have, now been applied to various fields from medicine to self driving cars to even farming. And it offers a promise to revolutionize software development and quality assurance in particular. My presentation will cover the following develop contents. Introduction, the shift in software development, the current state of code review and quality assurance. How is AI revolutionizing code review and QA, the key benefits of AI driven code review, its real world impacts, what are the quantifiable improvements. How is AI used in quality assurance and how is it transforming testing? we'll go through some case studies, success stories with AI driven code. Then we'll discuss challenges and consideration in AI adoption. And finally, I'll be presenting concluding remarks. Introduction, the shift in software development. The software industry is witnessing a fundamental shift driven by advances in AI and machine learning technologies. Traditional development methods involve manual code reviews and QA processes, which are time consuming and prone to errors. AI is not just enhancing these processes, it's transforming them by automating code analysis. predictive issues and providing insights that are difficult for human reviewers to catch. This shift represents a move towards more intelligent and data driven software engineering practices. The current state of code review and quality assurance. Manual code review relies heavily on individual expertise, leading to inconsistencies and varying qualities because different individuals may have different skill sets and level of quality and expertise. The bug detection rates are limited, with studies showing that around 60 to 70 percent of defects are found using the traditional methods. Which means 30 to 40 percent are not found. And the cost of fixing this defects grows exponentially when the issues are discovered later in the development life cycle, and especially post release. QA is a resource intensive process. It requires significant manual effort to maintain and update the test switch. An example, a study by Cisco systems found that traditional code reviews typically uncover only about 60 percent of defects leading to significant technical deaths. How is AI revolutionizing code review and QA? AI tools can analyze fast amounts of code with speed and accuracy that far exceeds human capabilities. Techniques like natural language processing, NLP, allow AI to understand code documentation and comments. which enhances the review process. Pattern recognition helps identify both common coding issues like memory leaks and more subtle security vulnerabilities. Real time feedback that AI offers enables developers to detect and fix problems as they code, reducing the chances of technical deaths piling up. The key point here is that AI doesn't just automate tasks. It enhances them by applying sophisticated analysis to detect issues earlier and more effectively. What are the key benefits of AI driven code review? AI driven code review enhances code quality. AI tools can identify potential bugs, vulnerabilities, and code smells leading to higher quality code. Accelerated development life cycles By automating code review and testing, AI reduces the time needed for this task, allowing for more rapid iteration of development iterations. Reduction in technical debt. Early reduction of issues means fewer problems down the road, saving cost and time in the long run. Predictive analytics. AI can anticipate potential bottlenecks and project completion times more accurately, allowing for better planning and resource allocation. Personalized assistance. AI driven development tools offer real time solutions and auto completions. Helping developers maintain consistent coding standards. Real world impact, what are the quantifiable improvements? First of all, bug detection rate. Using AI increases detection rates of bugs from 70 percent to 95%, which is a 35. 7 percent improvement. Demonstrating more comprehensive issue identification. Development life cycle time. AI was found to reduce development cycle from 12 weeks to 8 weeks, accelerating time to market by 33%. Code review time. AI cut code review from 8 hours to 2 hours per 1, 000 lines of code, showing a 75 percent time saving. Test generation time was decreased from 24 hours to 6 hours. which resulted in 75 percent reduction in the time it took to generate the test cases. Prediction accuracy for project bottlenecks. It improved from 60 percent to 85 percent helping avoid delays. Developer productivity increased from 100 to 150 lines of code per day, boosting output by 50%. Test coverage. The test coverage was raised from 75 percent to 95%. achieving a 26. 7 percent increase in comprehensive testing. The key takeaway here is that the integration of AI significantly enhances efficiency and quality across software development metrics. How is AI in QA transforming testing? AI automates test generation. It can automatically create test cases based on code changes. Ensuring thorough coverage with minimal effort. Like we don't have to keep on going back and adding, code, as the code base changes. AI can do that for us. Self healing test. As the user interface changes, AI power tools can update test scripts to reflect these changes, which reduces the need for manual update. Intelligent test execution. AI can prioritize test cases based on the likelihood of bugs, optimizing testing efforts and reducing time. Predictive analytics in testing. By analyzing historical test results, AI can predict the areas that are most likely to have defects, which allows QA teams to focus on critical parts of the code. Finally, the impact. Organizations using AI for QA have reported up to 50 percent reduction in testing time and a significant decrease in bugs. reaching production. Case study involving some tools. Amazon CodeGuru. This AI powered code review tool helped Amazon reduce application downtime by 50 percent by detecting critical issues that would have otherwise gone unnoticed. Static code analysis in continuous integration pipelines. By integrating AI tools in CI CD processes, companies have seen a 60 percent increase in defect detection rates. leading to more stable deployments. Deployment failures reduced by 60%. Organizations using AI powered analysis to monitor code changes have reported significantly lower failure rates in production releases. Takeaway, AI is not just a theoretical solution, it is delivering concrete measurable results in the real world. What are the challenges and considerations in AI adoption? The initial challenges are initial setup cost, implementing AI tools, required investment, upfront investment in new technology and testing, and training, which is expensive. Learning curve, development teams will need to, they will need time to ramp up and learn new ways of, testing and adapting to new workflow and AI powered tools. Data quality requirement. The effectiveness of AI tool depends on the quality of AI data available. initially we will need a good quality and quantity of data for training. What are the solutions to overcome these challenges in early adoption? Start small. Start by with small automated simple tasks like coding, code linting, or generating small test cases. Continuous learning. Choose AI tools that improve as more. Data becomes available, ensuring better accuracy over time. Hybrid approach. Combine AI automation with human oversight to achieve better results. The best of both worlds in code review and quality assurance. Conclusion, while there are challenges, the benefit of AI in software development outweigh the challenges and, make, overcoming these challenges worthwhile pursuit. Conclusion, the future of AI driven development. The key takeaways, AI is revolutionizing software development by enhancing quality, accelerating cycles, and enabling more productive insights. Thanks. Development teams can focus on creative problem solving and innovation as AI handles routine and complex tasks. Early adopters of AI driven methodologies gain a significant competitive advantage in the rapidly evolving tech landscape. Call to action. Embrace AI to revolutionize your software development process and stay ahead of competition. Thank you for joining me today for my presentation.