Can AI Replace Developers? What Software Development May Look Like in 5–10 Years
AI is gradually changing not only how we create software, but also how we perform the daily tasks associated with software development. Developers are already using artificial intelligence to generate code, debug, test, and document software. Individual auxiliary processes are also automated, such as checking user scripts using a virtual phone number for verification.
All of this raises a larger question: will AI be able to eventually be able to replace developers, or will it simply become a more powerful tool for them?
How AI Is Changing Software Development
Artificial intelligence is technology that can simulate certain aspects of human behavior. Therefore, it can replace human labor when performing certain tasks. For example, you can use it to write text, generate an image, modify a person’s voice in a recording, and much more. Neural networks are constantly improving and becoming increasingly popular. They are widely used because they simplify the work of many professionals, including those in the IT industry.
Popular Neural Networks and AI Tools:
- ChatGPT, Smolin, InferKit, Ya.Autopoet — can generate texts;
- DALL-E 2, Midjourney, Dream, IMAGINE — generate images;
- RemoveBg, Retoucher — remove background from images;
- Prisma Labs, Colorize — process photos;
- Looka, Namelix, AutoDraw — create brand names and logos;
- Bigjpg, AI.Image Enhancer — improve image quality.
People also use neural networks to create and animate photos, generate music, and recognize speech. They are actively used in various fields and help to solve numerous tasks. For example, in business, a neural network can analyze information about user preferences, create content, or help run advertising campaigns.
The field of software development is also increasingly incorporating artificial intelligence. With the help of AI, programmers can write code and perform various operations with it.
Generating Code from Natural Language
Neural networks most often support popular programming languages such as JavaScript, Java, C, and Python. AI-powered programming tools include: GitHub Copilot, Codeium, Tabnine, Amazon CodeWhisperer, GigaChat, Gemini and others. These are specialized AI tools that are designed to help developers write code. Unlike general-purpose AI tools, they understand the overall logic of a program. Such tools are also better at solving certain mathematical and programming problems. Here are some of the things they can do:
- Check code that has already been written by a programmer.
- Complete unfinished code.
- Generate code from scratch.
- Help write documentation.
Neural networks can also translate code from one programming language to another, train beginners, create web services and develop software prototypes.
AI-Assisted Code Completion
To reduce the time spent writing code, a programmer can start writing it themselves and ask an AI tool to complete the rest. Tabnine and Copilot are good at this task. They are able to adapt to a particular developer’s coding style and requirements and generate code that is similar to, similar to what the developer would write themselves.
When working with or updating code, it can be beneficial to use paid versions of AI tools. Compared with free versions, they may offer more advanced capabilities and can generate larger portions of code.
Improving Code Quality with AI
Optimization involves improving a program’s performance, and refactoring focuses on improving the structure, clarity, and maintainability of its code.
Refactoring helps developers:
- make code easier to read for all members of the development team, simplifying and speeding up further development;
- streamline their workflow;
- reduce the time needed to find and fix errors;
- preserve the original program structure.
Refactoring is necessary when it is difficult to maintain or extend existing code, when the same changes need to be made in several places and streamline the development process.
During the refactoring of program code, a programmer can: split classes into several smaller ones, remove ‘dead code’ — unnecessary sections of code, correct incorrect variable names, or reduce the number of methods and functions.
Neural networks help speed up the refactoring and optimization process. They can suggest possible options. This improves the quality and readability of the code. Examples include Adrenaline and Transform Code. The former is used for refactoring, and the latter is used for optimization. These tools can speed up the software development process.
AI-Powered Bug Detection and Debugging
Once the code for a program has been written, it is passed to a software tester. A tester is a specialist who checks the functionality of a website, application, or other software created by developers. If the tester finds errors, they document them and returns the product to the development team for revision.
Artificial intelligence can help here as well. For example, Adrenaline and Copilot neural networks can find errors and correct them. However, they do not always do so correctly. AI can also help with testing by automating routine tasks and answering questions that arise during the process.
Artificial intelligence is particularly good at handling tasks typically assigned to junior specialists. For example, writing checklists or describing test cases. The problem is that AI is still limited when it comes to more complex tasks. For example, it may struggle with more complex tasks.
Where AI Still Falls Short
- Understanding Context and Making Decisions. The work of artificial intelligence is based on large amounts of data used to train or inform it. If the available data is insufficient, the AI system may produce an inaccurate or completely incorrect result.
- Learning and Adapting Without Human Input. AI systems cannot independently add reliable new information to their knowledge base or determine which outdated information should be replaced.
- Acting Without Human Direction. AI systems can initiate tasks and produce results only within the instructions, prompts, or workflows provided by humans.
- Handling Creativity and Ambiguous Problems. AI can generate images or music on demand, but it will not be able to add genuine emotional nuance to text and determine the best way to structure ambiguous information.
- Verifying Facts and Understanding Context. AI systems work with the information available to them. If some facts are outdated and the system has not been updated, it may provide outdated information.
What the Future Holds for Software Developers
Artificial intelligence systems continue to evolve and may significantly change the field of information technology. But the software development profession remains in demand.
AI can automate some routine tasks and make programmers’ work easier. It can partially replace certain responsibilities. Nevertheless, its capabilities are not enough to replace mid-level and senior developers. Expert supervision and human expertise are still essential. Developers need to monitor AI-generated work, modify or redo parts of it when necessary.
