How Artificial Intelligence Contributes to Software Development

Software development is much more than writing code. Understanding the need, designing a solution, coding, testing, documenting and maintaining are steps that follow and feed one another. Large language models, and the code assistants built on them, are now being used as supporting tools in almost all of these steps. In this article we look at how artificial intelligence contributes to the software development process, where its limits lie, and what this means for businesses that commission software. Our aim is not to praise a tool but to set out clearly what can reasonably be expected and what cannot.

Requirements analysis and documentation

The most critical stage of a software project is understanding correctly what needs to be built. Meeting notes, e-mails and the workflows described by the customer often arrive scattered and incomplete. Artificial intelligence tools can be used to summarise this material, turn it into a structured list of requirements and flag statements that contradict each other.

Given a requirements text, a tool can also draft the questions that still need to be asked. If an approval flow is described without saying what happens to a rejected record, for example, the tool may point out the gap. Deciding which requirement really matters, however, remains the responsibility of the analyst who knows the business and the customer. The tool is a reminder of the question, while the answer comes from the person who talks to the customer.

Help with writing code

Tools known as code assistants can complete the line a developer is typing, produce a first draft of a described function or quickly generate repetitive boilerplate. They save time particularly with well-known patterns such as form validation, data transformation and simple listing screens. Instead of writing the same thing again and again, the developer can focus on the hard part of the job, namely the business rules and the architecture. Learning a new library or an unfamiliar language also becomes easier when there are examples to work from.

There is an important distinction to make here. AI-assisted software development does not mean having a tool write the code and using it as it is. Generated code may not fit the existing structure of the project, its naming conventions or its security rules. Every suggestion should therefore be treated as a draft, read by the developer and rewritten where necessary.

Code review and finding bugs

Code review, where code written by one developer is read by another, is one of the basic safeguards of quality. Artificial intelligence tools can join this process as an extra pair of eyes. They may warn about a missing null check, a resource that is never released, a condition built the wrong way round or a section that is hard to read.

Debugging benefits in a similar way. Given an error message and the related piece of code, a tool can list possible causes and suggest where to start looking. These suggestions are not always right, though. Because the tool does not see the whole system or the data in the live environment, it can produce an explanation that sounds plausible and is still wrong. It complements human review and does not replace it.

Generating tests

Writing tests is often the first thing to be dropped when a project is under time pressure. Artificial intelligence can help list the cases that should be tested for a function and draft the test code. It is especially useful as a reminder of the cases people tend to skip, such as boundary values, empty inputs and data in an unexpected format.

What needs attention is what the generated test actually verifies. A tool may assume that the current behaviour of the code is correct and write the test accordingly. If the code is wrong, the test ends up confirming the mistake. The expected result should therefore be defined by someone who knows the business rule. A large number of tests is not a sign of quality in itself either. What matters is that the right cases are tested.

Understanding and modernising legacy code

Many businesses rely on software that was written a long time ago by people who are no longer on the team. Reading that kind of code and working out what it does can take longer than adding a new feature. Artificial intelligence tools can be used to explain a section of code in plain language, map its dependencies and propose a step-by-step plan for modernising it.

Drafts also speed up tasks such as moving from an old language version to a newer one or making a tangled section more readable. Legacy systems, however, contain undocumented business rules that live only in the code. To avoid losing them during modernisation, changes should be made in small steps and each step should be tested.

Documentation and security scanning

Documentation is essential for software to have a long life, yet it is usually postponed. Artificial intelligence is useful for preparing technical descriptions, draft user guides or change summaries based on the code. Rather than starting from a blank page, the developer corrects a draft, which makes documentation easier to keep up. The draft still has to be checked against the code, because a wrong document can be more misleading than no document at all.

On the security side, tools can search the code for known vulnerability patterns:

• User input added to a query or to the output without being checked
• Passwords and access keys written into the code
• Missing authorisation checks
• Use of outdated components

These scans are a useful first filter. A clean scan, however, does not mean that the software is secure. Security has to be considered from the design stage onwards and calls for expert review.

How is the developer's role changing?

Rather than replacing the developer, artificial intelligence is shifting the centre of gravity of the job. As less time goes into typing code line by line, defining the problem correctly, designing the solution, evaluating the generated output and taking responsibility for the result come to the fore. In other words, asking good questions and reading the answers critically are becoming core skills.

This does not reduce the value of experience. It increases it. To notice that a suggestion is wrong, you need to know what right looks like. Domain knowledge, meaning familiarity with the customer's business and the rules of the sector, is something no tool can supply on its own. For people who are new to the profession, tools can speed up learning, but relying on suggestions without acquiring the fundamentals creates a weakness in the long run.

Why is human oversight essential?

Large language models answer in a fluent and confident tone, but fluency is not proof of accuracy. Tools may use a function that does not exist as if it did, present a faulty calculation as correct or guess on the basis of incomplete information. If such fabricated output reaches a live system without being checked, it can cause serious problems. The following points should be observed when artificial intelligence is used in software projects:

• Accuracy: Every piece of generated code should be read, run and tested.
• Data privacy: Customer data, passwords and code that counts as a trade secret should not be entered into tools whose terms are unknown.
• Responsibility: The team that delivers the software is responsible for its defects, not the tool.
• Consistency: Output should be brought into line with the architecture and rules of the project.

What does it mean for the customer?

For a business that commissions software, the contribution of artificial intelligence can be indirect but tangible. Faster handling of repetitive work may allow the team to test more cases in the same amount of time, keep documentation up to date and show a first working version earlier. An early version helps a misunderstood requirement come to light at the beginning of the project instead of at the end.

A useful question to ask when commissioning software is which rules the team follows when using these tools. Whether customer data is entered into a tool, who reviews the generated code and how tests are run should all be open to discussion. The label AI-assisted software is not a quality guarantee in itself. The guarantee comes from the process.

On the other hand, artificial intelligence does not rescue a poorly defined project. A clear description of the need, regular feedback and an experienced team remain the basic conditions for success. If you are looking for a solution tailored to your own processes, you can take a look at BYK Yazılım's custom software development service and share your requirements with us.