AI assistants are rapidly transforming the software development landscape, empowering engineers to write code more efficiently than ever before. In this interview, we'll explore some of the top AI tools with a seasoned mobile developer Ilia Zadiabin, who shares his insights on how these tools are revolutionizing the way software is built in 2024.
In general, software developers have looked favorably upon AI assistants, expecting that the new technology can improve productivity and smoothen their workflow. As an expert, could you tell us what exactly AI assistants do?
To explain briefly, AI assistants are facilitators in the performance of tasks, delivering information through natural language processing. For instance, AI assistants are useful to manage repetitive activities such as scheduling and data entry, plus they can answer questions, or interact with other apps to complete tasks seamlessly. The system learns user preferences to personalize its responses and it has an impact on user experience indeed. Generally speaking, even though AI assistants are becoming integral, their creators still strive for improvements in functionality and reliability.
What AI assistant tools are used in the development workflow? Which features do you believe are required for an AI assistant in case it has to work effectively for software engineers?
Well, in domains related to industries such as engineering and finance, optimization is achieved with domain-specific performance tailored accordingly.
AI Chatbots are the first to come to my mind. They provide the facility of text-based support on websites and other instant messaging platforms through natural language processing, which makes it easier to engage users.
Also, I can use famous conversational agents Alexa and Siri as good examples. They provide voice-activated interaction facilities for performing several functions.
Tools like GitHub Copilot and Tabnine are available for generating code, handling debugging, offering suggestions in real time, and improving the efficiency and quality of coding. The list goes on.
I see. Could you provide more details on how they help improve productivity in your field?
AI assistants make developers' experience better in many ways, helping to focus on what they are doing and, as a result. creating a more motivated workforce.
First of all, AI assistants offer the best coding practices and refactoring of existing code to maintain its quality. They also catch bugs, enforce best practices, and reduce technical debt.
Independent studies show that developers experience a productivity boost of as much as 45% when using AI coding assistants. On average, AI tools perform tasks such as code generation, refactoring, and documentation 20-50% faster than when they are executed manually.
Moreover, AI tools automate many of the mundane, repetitive tasks, allowing developers to focus on higher-level design and problem-solving, reducing stress and mistakes, and thereby enhancing productivity.
While AI assistants are helpful, at least in areas such as code completion and looking up solutions, the real productivity benefit goes much further than coding. I believe that improvements to communications and collaboration tools have even greater impacts on developer productivity.
What problems are encountered while working with AI Assistants? What ethical concerns do you think should be raised in working with AI in software development?
AI assistants can be dangerous, and risks are associated with cybersecurity and ethical concerns.
Skilled fraudsters can use AI assistants to mislead communications or conduct phishing attacks that may result in reputational harm or financial loss. Most of the information handled by AI assistants is sensitive. A single vulnerability may result in vast exposure of personal and confidential business information.
When it comes to regulated industries, the use of AI assistants is making it almost impossible to adhere to strict regulations on data handling thus exposing them to legal risks.
With the progress of autonomy in AI assistants, there is a rising risk that they may act against user intent. Misconceptions of instructions could lead to unintended consequences.
Moreover, AI Assistants might spread false information because they tend to come up with wrong answers, which can damage trust and may be harmful.
Is it possible to create your own AI assistant?
Yes, you can create your AI assistant, step-by-step. Decide what purpose you want your AI assistant to achieve. This could be anything from keeping track of schedules to answering questions.
Then, you should select a development platform or framework. You could use Python or other libraries, such as NLTK for natural language processing, or go for no-code platforms like Lindy to set things up more easily. You can develop voice recognition, text-to-speech, integrations with other APIs, etc.
Train your assistant on relevant datasets so that it understands the interactions and learns through them over time. Keep testing your assistant and fine-tune its functions continuously based on user feedback and performance metrics.
What place do you think AI assistants will take within the area of software development in a few years?
In a few years, I believe AI assistants will be core enablers of software development. As their functionality improves, they will support more sophisticated coding and further provide insight into the nature of software projects, significantly improving productivity. On top of that, by 2028, about 75% of all developers will be using AI assistants. This shows a behavioral change in low-code and AI-infused development platforms.
These tools improve not only the efficiency of coding but also enable developers to focus on higher-order tasks, continuous learning, and adaptation to a rapidly evolving tech landscape. In general, AI assistants are likely to enlarge the role of developers, promoting a collaborative environment in which coding will be more accessible to a wider audience.
