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Is your team using Generative AI to increase code quality, accelerate delivery, and reduce time spent per Sprint? Or are you still in the testing and testing phase? Wherever you are on this journey, you cannot deny the fact that Gen Ai is increasingly changing our reality today. Effective in writing code and performing related tasks such as testing and QA. Tools like GitHub Copilot, Chatgpt, and Tabnine help programmers with dynamic tasks and organize their work.
And this does not seem like the leading hype. According to the market future report, the AI generated software Development Lifecycle (SDLC) market is expected to increase from $0.25 billion in 2025 to $75.3 billion in 2035 billion in 2035.
Before giving birth to AI, an engineer had to extract requirements from long technical documents and assemblies by hand. Prepare UI / UX Mockups from scratch. Write and serve the error manually. Troubleshooting troubleshooting and log analysis.
But the entry of Gen AI has removed this document. Production is quiet. Repetition, manual labor is reduced. But underneath this, the real question remains: How did Ai transform the SDLC? In this article, we explore that and more.
Where Gen AI can work
LLMS proves to be the best 24/7 helpers in SDLC. It uses repetitive, time-consuming tasks. Free developer to focus on design, business logic, and innovation. Let's take a closer look at how Gen Ai adds value to the SDLC:
The possibilities with Gen AI in software development are both ambitious and overwhelming. It can help increase productivity and speed up turnaround times.
On the other side of the coin
While the benefits are hard to miss, it raises two questions.
First, about how secure is our information? Can we use the client's private information to download faster? Isn't it dangerous? What are the chances of chatgpt conversations being private? A recent investigation revealed that the Meta AI app marks private conversations as public, raising privacy concerns. This should be analyzed.
Second, and most importantly, what will be the future role of developers in the automation era? The onset of AI has affected many service sector profiles. From writing to producers, digital marketing, data entry, and many more. And some reports project a future that is different from the way we might have imagined it five years ago. Investigators at the US Treasury Department's Oak Ridge say those machines, rather than say, will write most of their code by 2040.
However, whether this will be the case is not part of our discussion today. Currently, as with other profiles, programmers will be required. But the nature of their work and the skills required will change somewhat. And that, we take with the gen ai hype check.
Where the hype meets reality
- The output generated is audible but not consistent (at least, not yet): with the help of Gen AI, engineers quickly report the iteration, especially when writing regular patterns. It can work for a well-defined problem or when the context is clear. However, with the new Logic, which is Domain-E specific and Performance-Critical, human oversight remains non-negotiable. You cannot rely on productive AI / LLM tools for such projects. For example, let's look at modern heritage. Systems like IBM AS400 and Cobol have powered businesses for many years. But over time, their effectiveness has been reduced as they don't match today's re-empowered user base. In order to maintain or improve their operations, you will need software developers who not only know how to work around those systems but are also updated with new technologies.
The organization cannot risk losing that data. Depending on Gen AI tools to build advanced applications that integrate seamlessly with these legacy systems will be too much to ask. This is where the expertise of programmers is always very important. Learn how to use smart legacy systems seamlessly with Agents ai. This is just one of the critical use cases. There are many, many things. So, yes llms can speed up the SDLC, but not as a replacement for the important cog, that is, people.
- Test automation has fallen quietly, but not without human supervision: LLMS excels at creating various test cases, visual gaps, and debugging. But that doesn't mean we can keep human cults out of the picture. Gen AI can't decide what to test or interpret failures. Because people are not expected, for example, an e-commerce order can be delayed for many reasons. And a customer who orders essential supplies before leaving the Mount Everest Base Camp Trek can expect the order to arrive before they leave. But if the Chatbot is not trained on content factors such as urgency, delivery, or different from the user's intent, it may fail to provide an empathetic or relevant response. The Gen AI testing tool may be able to test for such variations. This is where people's thinking, years of technological innovation, and long-term thinking are concerned.
- Literature has never been easy; But there is a catch: Gen Ai can auto-generate documents, summarize meeting notes, and do much more with a single stream. It can reduce time spent on manual, repetitive tasks, and provide consistency for large projects. However, it is impossible to make decisions. It lacks true judgment and spiritual maturity. For example, understanding why a certain symbol is written or how certain choices can affect future fitness. That's why how you interpret complex behavior is up to the programmers. They have been working on this for many years, building awareness and a solid understanding of replicating machines.
- AI continues to struggle with a real-world difficulty: limitations from context. Concerns about trust, overconfidence, and conformity. And the integration of the conflict is persistent. That's why CTOS, CTOOs, and programmers are skeptical about using AI in proprietary code without Guardrails. People are essential in providing context, proven results, and keeping AI in check. Because AI learns from historical patterns and data. And sometimes that data may reflect the world's imperfections. Finally, an AI solution needs to be ethical, responsible, and secure to use.
Final thoughts
A recent survey of more than 4,000 developers found that 76% of respondents agreed to challenge at least part of the AI-generated code before it was implemented. This shows that while technology improves convenience and comfort, it will not depend on perfection. Like other technologies, Gen AI also has limitations. However, to dismiss it as mere hype would not be entirely accurate. Because we have gone through how useful the device is. It can meet demand and planning, write code quickly, test multiple cases in seconds, and identify anomalies in real time. So, the key is to accept LLMS accordingly. Use it to reduce hard work without increasing risk. Most importantly, manage as an assistant, a “strategic driver”. Not a replacement for human technology.
Because ultimately, businesses are created by people for people. And Gen Ai can help you increase efficiency like never before, but relying on them alone to get good results may not create good results in the long run. What are your thoughts?
