Software Development AI’s Post

This AI-driven approach is transforming mundane coding tasks into automated processes, enabling developers to focus on more complex and creative aspects of programming software. Read more 👉 https://lttr.ai/AS2IH #AutomatedCodeReviews #GenerativeAi #UxDesign #ReactNative #SoftwareDevelopment #OffloadingRepetitiveTasks #DrasticallyReducingVulnerabilities #MlSPredictiveCapabilities #ManualReviewTime #MeansFasterTimeToMarket

This AI-driven approach is transforming mundane coding tasks into automated processes, enabling developers to focus on more complex and creative aspects of programming software. Read more 👉 https://lttr.ai/AS2IZ #AutomatedCodeReviews #GenerativeAi #UxDesign #ReactNative #SoftwareDevelopment #OffloadingRepetitiveTasks #DrasticallyReducingVulnerabilities #MlSPredictiveCapabilities #ManualReviewTime #MeansFasterTimeToMarket

Microsoft introduced AutoDev: Revolutionizing software development with AI-driven automation. 👐 Transcends traditional code suggestions, offering full-spectrum software engineering tasks. 🤝 Enhances efficiency and security within Docker containers for a seamless development experience. 👌 Demonstrated success: 91.5% in code generation, 87.8% in test generation using the HumanEval dataset. 🤘 A game-changer for developers, shifting focus to strategic tasks with AI handling complex operations. When it rains it pours💦 #GenAI #Python #Java

Software Development 2024: Trends Shaping the Future: https://lttr.ai/ASpGb #AutomatedCodeReviews #GenerativeAi #UxDesign #ReactNative #SoftwareDevelopment #OffloadingRepetitiveTasks #DrasticallyReducingVulnerabilities #MlSPredictiveCapabilities #ManualReviewTime #MeansFasterTimeToMarket

Software Development 2024: Trends Shaping the Future: https://lttr.ai/ASoZx #AutomatedCodeReviews #GenerativeAi #UxDesign #ReactNative #SoftwareDevelopment #OffloadingRepetitiveTasks #DrasticallyReducingVulnerabilities #MlSPredictiveCapabilities #ManualReviewTime #MeansFasterTimeToMarket

9 Top Trends In Software Development For 2024 You Need To Know 👉 The Rise of Retrieval-Augmented Generation (RAG) Apps 👉 API-First and Headless Architecture: Transforming Software Flexibility 👉 impact of AI-Driven Development Tools on the Software Ecosystem 👉 AI’s Role in Making Coding Accessible to Everyone 👉 Cybersecurity Automation Enhances Development 👉 Low-Code Platforms Empower Non-Developers 👉 LLMs and Their Influence on Code Generation 👉 Generative AI Shapes the Future of Software Development 👉 AI-Assisted Coding Tools: Transforming the Development Landscape 👉 AI-Powered Tools: Accelerating Software Creation at Unprecedented Speed #jhkinfotech #software #softwaredevelopment #AI #lowcode #LLM

CodeMaker AI Breakthrough in Software Development: Achieves 91% Accuracy in Recreating 90,000 Lines of Code, Setting a New Benchmark for AI-driven code Generation and Fine-Tuned Model In an era of AI-transforming industries, CodeMaker AI has achieved a landmark breakthrough by autonomously recreating a 90,000-line software library with an astounding 91% similarity to the original codebase. This achievement marks a significant shift in how AI can be utilized in software development, demonstrating the potential to reduce manual coding efforts and accelerate development timelines drastically. The CodeMaker AI is fine-tuned to understand and generate complex code structures, processes over 3,200 files, and reproduces the code in under two hours. By leveraging advanced machine learning techniques, CodeMaker AI has shown that large-scale code generation, once arduous for human developers, can now be achieved with precision, speed, and cost-effectiveness. The implications of this development extend far beyond simple code generation, as it represents a new frontier in AI’s role in automating and augmenting complex tasks within the software engineering landscape. CodeMaker AI: The Experiment The core of CodeMaker AI’s experiment involved fine-tuning a machine learning model specifically on a codebase, allowing the AI to generate code autonomously. Fine-tuning refers to taking a pre-trained model and further training it on a specific dataset to adapt it to a particular task. For this project, the AI was fine-tuned on a full production codebase, making it capable of generating code that aligns with specific coding styles, domain spaces, and structure. The recreated code was published on GitHub for public scrutiny, and estimates based on the COCOMO model suggest that manually recreating the code would have taken around 25 years of developer time. This stark comparison underlines the efficiency AI brings to software development. Fine-Tuning Process The fine-tuning process involved training the AI model on 129 million tokens from the codebase, which took 11 hours and 44 minutes for $1949.75. The model was then used to recreate the erased code in the `src/main/java` directory using CodeMaker AI’s batch code generation feature. The command used for this operation was: —bash codemaker generate code --model user-model **/src/main/**/*.java This batch generation process was completed in 1 hour and 42 minutes, showcasing the efficiency of CodeMaker AI in large-scale code generation tasks. Code Comparison and Evaluation To assess the accuracy of the AI-generated code, CodeMaker AI employed two key metrics: error rate and similarity rate. The error rate was defined as the Levenshtein distance between the original and generated files, measuring how far apart the two files were. The similarity rate was calculated as follows: —Python similarity_rate = 1 - (dist(a, b) / max(len(a), len(b))) This metric answered the question of how similar...

It’s no surprise that generative AI is revolutionizing how people work across industries – and for software development, it’s turning out to be a true game changer. From productivity gains to QA support, Gen AI is helping developers handle everything from basic programming tasks to data acquisition and analysis. Applause CTO Rob Mason explores the use cases and strategies for success. Learn more in his blog post: https://bit.ly/4b9xQup #QualityAssurance #DevOps

Fixing bugs is a challenging task that directly impacts quality. Improper testing can result in increased costs and inefficiencies, particularly when dealing with APIs. At Knowl, we can assist you in resolving bugs more effectively. Read our blog to learn more. — #AIinAPI #api #apidocumentation #APIs #SoftwareDevelopment #Debugging #Documentation #TechStartup #DeveloperTools #CodeQuality #SoftwareEngineering #AI #Automation #Knowl #TechInnovation #DeveloperCommunity #programming #techBlog

AI-Powered Software Development Will Enhance Developers’ Skills