Tag: AI in software development

  • We’re Not Being Replaced. We’re Inventing What Comes Next, Including Ourselves.

    We’re Not Being Replaced. We’re Inventing What Comes Next, Including Ourselves.

    After graduating with degrees in computer science and business in hand, I entered the software industry with the energy and optimism of someone ready to create great software. My first job wasn’t called “software developer”—I was hired as a systems engineer. And that title mattered, a lot more than I first realized.

    Right after being hired, I was sent through a formal systems engineering program and was immediately placed on a project that would become the foundation of a new division: Future Banking Systems. After two years of heads-down redesigning and building banking systems in Garden City, New York, I was shipped to Dallas, Texas, for an “exciting opportunity” in a new group just being formed.

    Turns out the new group began with me and one salesperson, starting from zero, to form the foundation for a group that would create banking systems for an industry experiencing massive disruption due to deregulation. Our current customers were disappearing because of acquisitions, which were rolled up to become financial institutions that were beyond the scale and capabilities of our current products and business model.

    Inventing Without A Net

    There was no approved business plan. No product definition documents. No product roadmap. No backlog. Just a vision document, leaders with foresight standing beside us, and the knowledge that the industry was headed somewhere new and our current path wouldn’t get us there. However much we thought it would cost, we instinctively knew it would probably cost 4x more. It was our responsibility to build systems for much larger financial institutions in an industry still very much in flux.

    We weren’t just creating next-gen banking applications. We were inventing systems for where the industry was going to be, not where it was. It was early, messy, and it was foundational. And it eventually led me into my first experiences with AI, developing in Lisp, Prolog, and expert systems while teaching database theory. Those formative experiences taught me something that’s stayed with me ever since: Creating software isn’t just about shipping code. It’s about inventing what comes next.

     

     

    That mindset—the instinct to look ahead, to build with intent—is what real engineering is about.

    Today’s AI Moment

    Fast forward several decades…We’re now in one of the most disruptive and transformative moments in software history. AI can write code, scaffold entire applications, generate test cases, and devise solutions to problems in seconds. Developers are using copilots, models, and mega-prompts to accelerate their work at a pace we’ve never seen. And agents are coming online, backed by models with reasoning capabilities, to automate development tasks and tackle harder problems.

    And with this combination of exuberant expectations and feverous innovation in AI, comes a familiar claim: “We won’t need developers anymore.”

    I couldn’t disagree more.

    Yes, AI can generate code. But just code isn’t the application, system, or product. Underlying every working application is a complex system. There are architectural decisions, engineering challenges, design trade-offs, failure modes, built-testing, security constraints, integration boundaries, long-term lifecycle concerns, and, oh yes, operability. Those don’t just come from better context and prompts.

    AI massively expands what’s possible. And software development is the landing zone for AI innovations. (See Tech Vendors See the Future – Software Development Is the Agentic AI-Proving Ground.) But someone still has to know what to build, why to make it, when to create what, and for whom, and how to keep it working when the world around it continuously changes. Today’s design decisions are tomorrow’s technical debt.

    What Software Engineering Really Means

    Leading software teams taught me that engineering is the art behind software. It’s where experience, discipline, creativity, and instinct converge.

    When a team is deep in shipping the release with heads down, checking in code, solving bugs, driving toward delivery, engineering leaders raise periscope. You have to scan for the problems that haven’t surfaced yet, the threats and opportunities forming at the edge of the horizon, and verify that our instrumentation matches course, depth, and speed. That’s engineering. It’s not just watching the project plan or tracking how many issues are in Jira. It’s reading the signals others might miss.

    I’ve learned to trust that instinct: the kind of bugs we’re finding, the questions we’re not asking, the problems we haven’t had to solve yet. These are clues. They tell me if we’re still on track, or if we are behind, even when the project status says otherwise. Engineers know to pay attention to how we’re progressing, not just what we’ve done and how fast.

    Engineering is more than shipping by a deadline. It’s knowing when the real blockers will hit and deciding when to face them. It’s applying instinct in tangible ways: adjusting the design, pulling forward a hard dependency, probing deeper when something hard is too easy. This isn’t about perfectionism. It’s about intent. It’s about leading a system into existence, not following a script or just building what’s asked.

    We Lead the Loop

    I’ll say it again: I take issue with the idea that AI will simply replace developers. We hear the phrase “human in the loop” tossed around like it’s some kind of failsafe. But that’s not what this is.

    Humans aren’t destined to just be in the loop, supervising AI. We lead the loop, and where the loop is going.

    We define the problem. We decide what great, good, and bad looks like. We understand when to trust, when to probe, when to double-check, and when to ask if we are asking the right questions. We raise the periscope. And that’s what this moment means more than ever.

    Skating to Where the Puck Will Be

    AI is where the puck is. But engineering? That’s how we skate to where the puck will be.

    And more than that, it’s how we set the puck on a new arc entirely. That’s what we’re here to do now: not just follow the capabilities of AI, but lead us into new innovations in how software is created, new architectures, new apps, and systems we wouldn’t have imagined. We are the force. AI is the force multiplier.

    That’s what excites me about this moment. We’re not automating ourselves out of relevance. We’re stepping into a higher role: engineers with expanded reach, clearer intent, and tools that extend our vision—not replace it. The AI IDE of the (near) future looks more like the game Starcraft, engaging through language, voice, and keyboard, versus today’s code editor and NLP interface hybrid.

    We’re Being Called Forward

    For those of us who’ve built real systems, who’ve led teams through impossible delivery schedules, who’ve watched the intangibles reveal deeper design flaws—we know what this era asks of us.

    It’s not about writing every line. It’s about knowing that what we are doing ultimately matters.

    It’s about being the one who sees the storm before it hits. The one who adjusts course, not because it’s easy, but because it’s necessary.

    AI will continue to evolve. And so will we. So will all the roles across the software development lifecycle. AI is raising the bar for engineering, but remember…

    We lead the loop.

     

    Mitch Ashley is VP and Practice Lead of Software Lifecycle Engineering at The Futurum Group. The voice of “AI across the SDLC”, Mitch is a serial-CTO, speaker, advisor, entrepreneur, and product creator. He leads analyst coverage of the Software Development Cycle (SDLC), with emphasis on AI-native and agent development, cloud-native, DevOps, platform engineering, and software security.

    See Mitch’s analyst research on the Futurum website.

    Subscribers can access Mitch’s Software Engineering Lifecycle practice
    decision-maker data, insight reports, and advisories through the Futurum Intelligence Platform.

  • 10 Key Features of AI Code Assistants

    10 Key Features of AI Code Assistants

    One of the most potent tools that have emerged to streamline the coding process is code assistants. Code assistants are software tools designed to assist programmers by providing suggestions and automated features that help in writing code more efficiently and accurately. They act as virtual aides, making the coding process faster, more intuitive and less prone to errors.

    Code assistants can be categorized into two types: Rule-based and AI-based. Rule-based assistants follow predefined rules and perform according to set guidelines. They are mainly used for syntax highlighting, code formatting and basic error detection. AI-based code assistants can learn from past coding patterns, predict what a programmer is trying to achieve and suggest appropriate code snippets. They are capable of understanding the context of the code and providing accurate suggestions, as well as generating entire blocks of code based on natural language instructions. Read this blog post for examples of popular AI coding assistants.

    Whether you are an experienced software developer handling complex projects or a beginner, coding assistants can automate the tedious aspects of coding, allowing you to focus more on problem-solving and innovation.

    The Growing Importance of AI in Software Development

    As software development becomes more complex and time-sensitive, the role of AI in this field has grown significantly. AI-driven code assistants represent a major shift in how software is developed. They bridge the gap between human expertise and the efficiency of automated processes. 

    AI in software development not only speeds up the coding process but also enhances the quality of the code. These tools can analyze vast amounts of code, learn from it and apply that learning to assist developers in real-time. They can adapt to different coding styles and requirements, making them versatile tools for a wide range of programming tasks. Furthermore, as software projects increase in size and complexity, AI code assistants become crucial in managing and navigating these complexities. They offer intelligent insights, detect potential issues before they become problematic, and assist in keeping large codebases clean and manageable.

    In addition to increasing efficiency, AI code assistants are instrumental in reducing the barrier to entry in software development. They make coding more accessible to people with varying levels of expertise, including those who are new to programming. This accessibility is essential in an era where the demand for software development skills is continuously rising. By providing real-time assistance and learning resources, AI code assistants are not just tools for current developers but also learning aids for future generations of programmers.

    10 Key Features to Expect from Code Assistants in 2024

    With the rapid advances in large language models (LLMs), the following features are becoming standard in coding assistants as we enter 2024.

    Advanced Contextual Understanding

    Modern code assistants are evolving to understand the context of code more intricately. Unlike simple autocomplete tools, these AI systems can analyze the broader scope of a project, recognizing patterns and offering suggestions that align with the overall architecture and design patterns. This advanced understanding not only streamlines coding but also aids in maintaining consistency throughout the codebase.

    Real-Time Code Optimization Suggestions

    AI tools can proactively recommend changes to improve performance and efficiency. For instance, a code assistant might suggest a more efficient algorithm or highlight redundant code blocks that can be simplified. This feature not only enhances code quality but also serves as a learning tool for developers, exposing them to best practices and alternative approaches.

    Enhanced Debugging Assistance

    Debugging is often a time-consuming aspect of development. In 2024, expect code assistants to revolutionize this process by offering enhanced debugging assistance. Through deep analysis, these tools automatically detect errors and anomalies, suggesting potential fixes. They can pinpoint the exact location of a bug and provide context, significantly reducing the time developers spend in the debugging phase.

    Automated Refactoring

    Refactoring is crucial for maintaining clean code, and AI-driven assistants now offer sophisticated automated refactoring capabilities. They intelligently suggest changes that improve code readability and maintainability, such as restructuring code or renaming variables for clarity. These suggestions will be particularly beneficial for managing and updating legacy code, ensuring it adheres to modern coding standards.

    Code Documentation Generation and Update

    Keeping documentation updated is a challenge. Code assistants can now automate this task by generating and updating documentation in real-time. As code changes, the documentation will reflect these updates, ensuring that it always stays in sync with the codebase. This feature will be invaluable in large teams where keeping track of changes can be daunting.

    Predictive Coding Assistance

    Moving beyond reactive suggestions, code assistants will offer predictive assistance. By analyzing coding patterns and project structures, these tools will predict a developer’s next steps and offer relevant suggestions even before they are explicitly requested. This proactive approach will speed up the development process, reducing the cognitive load on developers.

    Understanding and Responding to Developer Queries in Natural Language

    Developers can pose queries or express coding requirements in plain English, and the assistant will interpret and execute these requests. Chat-based interfaces offered by leading AI coding assistants take this one step further. This advancement will make coding more intuitive and accessible, especially for beginners or non-expert users.

    Real-Time Suggestions and Conflict Resolution in Team Projects

    Collaboration in coding projects can lead to conflicts and inconsistencies. Code assistants now offer real-time suggestions and automated conflict resolution for team projects. These tools will identify potential conflicts in merge requests and suggest optimal resolutions, enhancing team collaboration and maintaining code integrity.

    Personalized Coding Assistance Based on Past Interactions

    Personalization is becoming a significant feature of code assistants. By learning from past interactions with a developer, these tools will tailor their suggestions and support to align with individual coding styles and preferences. This adaptive learning approach ensures that the assistance provided is most relevant and efficient for each developer.

    Enhancing Accessibility of Coding for Non-Expert Users

    One of the most impactful features of code assistants is their role in democratizing coding. By simplifying complex coding concepts and automating mundane tasks, these tools will make software development more accessible to non-experts. Beginners and those learning new programming languages will find these assistants particularly useful, as they provide guidance and learning aids.

    Conclusion

    Code assistants powered by AI are becoming an integral part of the software development process. They not only make coding faster and more efficient but also reduce the chances of errors, making the resultant software more reliable. The role of code assistants is only expected to grow, bringing in more advanced and intuitive features. Whether you are a seasoned programmer or a newbie, embracing code assistants can be a game-changer for your coding journey.