Struggling with a sluggish SDLC? Generative AI can be your game-changer! Ascendion leverages this powerful tech to auto-generate code, automate testing & debugging, and optimize performance for a 30-60% productivity boost across your entire SDLC. Say goodbye to tedious tasks and hello to a future-proof development process. Contact Ascendion today and unlock the power of Generative AI: https://lnkd.in/e6iHx422 #GenerativeAI #SDLC #EngineeringAI
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CRO @ DiffBlue | Data Driven GTM Strategy | The best use case for AI anywhere in business | Helping software engineering leaders release quality code much faster
Generative AI for Software Engineering marks a huge shift in how we approach building software. It's not just about automation; it's about streamlining the entire software development lifecycle (SDLC). From crafting UIs and tests to documentation, GenAI alleviates the burden of manual-intensive tasks, freeing up developers to tackle more intricate challenges. By automating code completion and bug fixes, it accelerates the development process, slashing time-to-market for products. The rise of GenAI isn't just a trend; it's a game-changer that's democratising access to efficient software development tools and revolutionising the industry as we know it. #GenerativeAI #SoftwareDevelopment #Innovation
In Simple Terms: Generative AI For Software Engineering
financialexpress.com
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It was a great fireside chat https://lnkd.in/gZbemd_k with Kelly Ehret at #sfelc about how AI is helping all aspects of SDLC. Github co-pilot gets a lot of marketing but other areas of SDLC are seeing similar AI investments by many innovative companies - Project Management is seeing massive AI investments with a lot of mundane tasks about managing a project going away with AI - Testing has seen a massive turn-around with unit tests, functional tests and even UI tests being generated using AI - Deployment is another area where companies are using AI to generate a lot of deployment scripts and automatically flag PRs that can cause failures - APM is seeing AI tools sit on top of these APM tools and Git to tie an error spike or a latency spike to the actual PR I talked to a lot of Eng leaders at ELC and everyone was either experimenting or exploring AI tools in all the areas of SDLC. My only problem right now is that these are very fragmented today and quality varies significantly - hopefully it improves soon. I highly recommend every one to try AI tools in your SDLC and make it fun for developers to build code! #ELC #ClickUp #leadership #sfelc
How Engineering Teams Can Use AI to Improve Productivity by Shailesh Kumar,Kelly Ehret
sfelc.com
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🤖 Register today and join us live on the 12th of October for our webinar with Katalon, Artificial Intelligence: The 4th Amigo in Agile Software Development? https://hubs.li/Q023p_6g0 This webinar, featuring George Blundell, Rich Jordan, Mush Honda and Lucio Daza, will address three challenges to solve BEFORE applying AI across your CI/CD pipeline: ✅ Domain Knowledge: Learn how to give AI the structured data it needs to avoid defects. ✅ Test Coverage: Understand how to target AI test generation to find bugs that are often missed. ✅ Collaboration: Discover how AI can automate individual tasks without introducing silos, miscommunications, and lengthy feedback loops. You don't want to miss this! Register today for live and on-demand access: https://hubs.li/Q023p_6g0 #devops #tdm #testing #artificialintelligence #softwaretesting #qualityassurance #webinar #testautomation #softwaredevelopment #QA #modelbasedtesting #AI
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Will AI Force Us Back to the Waterfall Model? I've been researching how AI can be applied to the system requirements generation, tracing, and validation process in strictly regulated environments where development still follows the waterfall model. Yes, it suffers from rigidity and long implementation cycles, but it results in large amounts of high-quality context, and isn't that what AI needs? Feeding AI with high-quality data can help us compensate for rigid, long development cycles by accelerating the iteration. Maybe it's time to revisit our love-hate relationship with agile processes and invent new, AI-first software development process, even if it means adopting some of the waterfall habits again? Thoughts? #ArtificialIntelligence #SoftwareDevelopment #WaterfallModel #AgileMethodology
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I recently attended the "AI in SDLC" Roundtable, organized by DeVC. It was an amazing experience covering the role of AI in shaping the future of software development. The event featured great speakers, including Shailendra Sharma (NebulaIQ.AI), Kaushik Mukherjee (Raptorise), Naresh Agarwal (Traceable), and Amartya Jha (CodeAnt AI (YC W24). Their experience highlighted the transformative role AI can play in various phases of the Software Development Lifecycle (SDLC): Documentation and generating reports Automated Code Generation Fast Testing Procedures Streamlined Software Deployment Improved Security and Code Quality Key insights from the panel discussion include: Transforming Maintenance Costs: With more than half of software costs associated with maintenance, AI's impact on cost reduction, quality improvement, and developer experience is transformative. AI and Deterministic Methods: Integrating AI with deterministic approaches is essential for reducing hallucinations and ensuring precise outcomes, particularly in enterprise applications. Enhanced Developer Productivity: AI tools significantly decrease the time to understand code and resolve errors, leading to substantial productivity gains for development teams. AI as a Collaborative Tool: AI's potential is maximized when it handles repetitive tasks like test case generation and documentation, etc. This event provided valuable insights into the transformative role of AI in software development and application of AI technologies like Generative AI in the industry. #AIinSDLC #Innovation #TechRoundtable #FutureOfTech #AI #MachineLearning #GenerativeAI #GenAI #SDLC #SoftwareDevelopment Original post: https://lnkd.in/gd32iKev
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The software development landscape is a high-speed race for efficiency, speed, and reliability. CI/CD pipelines have been the workhorses, automating tasks like building, testing, and deploying code. But buckle up, because AI and machine learning (ML) are taking the wheel and transforming CI/CD! AI Supercharges Your Pipeline ML is enabling smarter automation across various stages. Here's how AI is revving your CI/CD engine: Smarter Code Review: Human + Machine Collaboration - Imagine an AI assistant that analyzes code, identifies issues, and even suggests improvements. ML algorithms scan for common pitfalls, reducing reviewer burden and accelerating development. This human-machine collaboration leads to more robust and secure code. Anomaly Detection: Catch Issues Before Takeoff - ML excels at identifying patterns and deviations. In CI/CD, this translates to detecting anomalies in build times, test results, or deployment behavior. Think of it as a watchful guardian for your pipeline. Proactive identification allows developers to address issues before they become major roadblocks, saving time and resources. Self-Healing Deployments: The Pipeline Doctor - Wouldn't it be amazing if your CI/CD pipeline could self-heal? AI-powered self-healing deployments are making this a reality. By analyzing historical data and error patterns, ML models can take corrective actions like restarting a failed build or rolling back a problematic deployment. Imagine a pipeline that diagnoses and fixes itself, minimizing downtime and ensuring smooth deployments. The Benefits of the AI Revolution The advantages of AI-powered CI/CD are extensive: Freeing Up Developers: Repetitive tasks are handled by AI, allowing developers to focus on innovation and problem-solving. Improved Reliability & Efficiency: ML proactively identifies and addresses issues, leading to more reliable and efficient deployments. Faster Time to Market: With AI streamlining CI/CD, teams can iterate faster and deliver features quicker, allowing businesses to stay ahead of the curve. The Road Ahead This is just the starting line of the AI revolution in CI/CD. As ML evolves, we can expect even more sophisticated automation. These advancements will further streamline software development and delivery, propelling businesses towards greater success. Are you ready to join the AI revolution? Share your thoughts and experiences with AI in CI/CD in the comments below! #devops #cicd #machinelearning #artificialintelligence
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Test Automation with Industry Experts & Founder of Test Guild | DevOps | Software Testing | Join our 40K Community | Podcast Host & Speaker | Follow ➜ #TestGuild | Become a TestGuild Sponsor 👇
Is the rapid AI evolution leaving your software testing practices in the dust? 😟 The landscape of DevOps is changing, and with it, the approach to software testing must evolve. Join us for a groundbreaking training session with Guy Arieli and Tal Barmeir from Blinq.io on April 16, 2024, at 10 AM EDT. This session promises to demystify AI's impact on software testing and introduce virtual testers to the field. What you'll learn: ✅ Upgrading manual testers to automation experts effortlessly ✅ Managing testing pipelines more efficiently with AI ✅ Embracing global capabilities with multi-lingual AI engines Sign up now to secure your spot in redefining software testing! 👇👇👇 https://lnkd.in/eJvG66GZ #AI #aiintesting #artificialintelligence #DevOps #AIinDevOps #virtualtester #softwaretesting #testautomation #automationtesting #automatedtestscript #multilingualtesting #virtualtest #scalabletesting #ondemandtesting #BlinqIO #testguildtrainingsession
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Renaissance Technologist / Coach (Agile, Transformation, and Engineering) / AI Enthusiast (Prompt Engineering) / Consultant
Unleashing Engineering Efficiency with AI! The world of software development is fast-paced, demanding continuous improvement and innovation from our teams. But what if there was a way to supercharge engineer workflows and unlock their full potential? Enter Artificial Intelligence (AI)! Here's a glimpse into some exciting possibilities: 1. Turbocharge Tedious Tasks: Imagine saying goodbye to repetitive tasks like writing boilerplate code or crafting unit tests. AI-powered tools like GitHub Copilot and TabNine can suggest code completions, while Pester or DeepTest can automate unit test generation. This frees up valuable engineer time for more strategic problem-solving and innovation. 2. Level Up Code Quality: Tired of battling bugs and maintaining code quality? AI can be your secret weapon! Tools like CodeClimate and Deep Code offer automated code reviews, identifying potential bugs, security vulnerabilities, and adherence to coding standards. Plus, AI can help with early bug detection, preventing issues before they cause headaches. 3. Fuel Innovation and Efficiency: AI isn't just about fixing problems - it can also fuel innovation. By analyzing data related to code commits, bug reports, and application performance, AI can identify trends and areas for improvement. This empowers engineers to make data-driven decisions and optimize their workflows for maximum efficiency. But wait, there's more! Spaghetti code, poor documentation, and a lack of unit tests can create a maintenance nightmare. By fostering a culture of clean code and collaboration, we can empower engineers to write code that's not only efficient but also sustainable. The Future of Engineering is AI-Powered! By embracing AI and fostering a culture of continuous learning, we can unlock a new era of engineering excellence. I'm always looking to learn and connect with other industry leaders. Visit my profile for more of my insights on Agile, Digital Transformation, Engineering practices and other topics! #AI #engineering #softwaredevelopment #innovation #neooutcomesllc
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📐 𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀: 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗣𝘆𝗿𝗮𝗺𝗶𝗱 📐 The traditional test automation pyramid emphasizes a foundation of unit tests, followed by service and UI tests, to achieve a balanced testing strategy. However, agile methodologies and continuous delivery practices have accelerated software development, challenging the rigid structure of the traditional pyramid model❗ Cue AI 👇 AI and machine learning technologies are revolutionizing test automation by enabling smarter test generation, execution, and maintenance. 🤖 Emerging testing models advocate for a more flexible and adaptive approach that integrates AI-driven testing techniques to meet the demands of modern software development. 🌟 #TestAutomation #QualityAssurance #QualityEngineering
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Generative AI (GenAI) has the potential to revolutionize software development by enhancing productivity and innovation across the entire lifecycle. However, adoption is limited due to factors like developer inexperience, organizational resistance, and technical challenges. To unlock GenAI’s full value, companies need to implement comprehensive change management, invest in developer training, and integrate GenAI into all stages of development. By doing so, they can achieve significant efficiency gains and faster market delivery
The Art of Scaling GenAI in Software
bcg.com
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