Although artificial intelligence models have been in development for many years now, it wasn’t until the creation of OpenAI’s ChatGPT that it truly entered the mainstream. Due to its many applications, AI is impacting almost all industries, including software development. In fact, 44% of software developers now use AI in their development process, with many planning to do so in the future.
In this article, we explore how artificial intelligence is changing software development, as well as the technology’s use cases, benefits, and challenges. Keep reading to learn more.
How Is AI Changing Software Development?
AI is positively changing software development in numerous ways. Here are just a few ways in which AI is enhancing the industry.
AI-Powered Code Generation
Artificial intelligence-powered content generators like ChatGPT and Claude.ai can be used to generate usable code in a number of development languages, such as Python and C#. This is not to say that it’s always entirely accurate, but with a quick review, AI generators quickly produce high-quality code. They can also provide instructions on how to compile and integrate the code with applications in development.
This makes the development process much swifter, as developers can hand off basic, and some complex, coding to artificial intelligence. With the correct integration, AI generators can even be used to write and compile code directly within codebases, adding another layer of convenience to the development process.
AI Testing and Bug Detection
Software developers can feed their code into AI generators and have it identify and correct bugs. For example, here’s a basic piece of Python code that contains a mistake:
When asking Claude.ai to identify the error, it returns:
|The line is missing a closing parenthesis after “hello”. In Python, print() is a function call that requires parentheses around its arguments.
So the code should be:
The corrected version encloses the string in double quotes and includes both the opening and closing parentheses for the print() function call.
This is just a basic example. So, let’s now up the ante with a more complex piece of code laden with error:
return x + y
if mark >= 50
print(“Flag is set!”)
When asked to identify the issues with this code, Claude.ai responds:
|There are several issues with this Python code:
print(“Flag is set!”)
To fix the issues:
Fascinatingly, Claude.ai is not just identifying errors based on the rules it understands for the Python programming language. It is also able to decipher the purpose of the code.
|This code defines a function to add two numbers, and implements some conditional logic to print messages based on variable values and conditions. It’s showing basic Python syntax for functions, if statements, and conditional printing.
The code has some improper indentation and syntax errors, but the main purpose seems to be demonstrating Python basics like functions, conditions, boolean variables, and printing.
This understanding of context means that AI generators can more effectively debug code since they know how the code should behave beyond the basic rules of programming languages.
Predicting Timelines and Budgets
Artificial intelligence models run by collecting, processing, and analyzing data, including historical time estimates and budgets. Using this historical data, AI generators can predict project development timelines and budgets based on new and historical developer time estimates while considering a margin of error by cross-referencing estimates with actual expended hours.
This ultimately ensures developers can more accurately provide clients with hourly estimates and bill them accordingly, reducing the chance of them having to take a loss or frustrate clients by billing more hours than expected. As a result, AI is an excellent tool for maintaining a reliable reputation and keeping clients happy.
AI Capabilities Transforming Software Applications
While the above section outlines how AI is changing the software development process, let’s now take a look at how AI is transforming software applications themselves.
Smarter Chatbots and Virtual Assistants
Artificial intelligence is allowing developers to integrate smarter chatbots and virtual assistants into applications. For instance, in the past, customer service bots could only provide customers with limited support, as they were restricted to a number of predefined responses. Now, thanks to large language models (LLMs) like GPT-4, customer services bot can provide customers with an infinity of contextually relevant responses to their queries.
With smarter chatbots, business owners don’t have to hire an entire team of customer service agents, improving the customer service experience at a lower cost. What’s more, in the instances in which human support is required, AI chatbots can be trained to identify these situations and hand them off to real service agents.
Building applications that have features tailored to the specific user is an excellent way to create an outstanding user experience that keeps people coming back. The language learning app, Duolingo, for example, leverages artificial intelligence to deliver different lessons based on each user’s individual ability.
Software developers can use artificial intelligence and machine learning models to better understand users and increase personalization, providing content and features specific to their interests. However, as with all AI models, personalization algorithms require training. As a result, increased personalization can only function optimally after gaining an understanding of the user’s preferences, which can be achieved through general software use or on-boarding surveys.
Advanced Predictive Analytics
Using AI to better predict timelines and budgets, as we discussed earlier, can also be integrated into client software projects. For instance, the technology can be utilized to allow business users to more easily interpret information by outline patterns and offering predictions. This is particularly useful in data-driven and enterprise-level applications that offer data reporting and business insights (BI) functionality.
Challenges and Concerns of AI In Software Development
Although artificial intelligence offers many advantages that can help ease the software development process, it’s not without its challenges. This means understanding the potential risks behind AI is imperative before implementing it into your development workflow.
Potential Job Losses
If you follow AI news, then you’ve likely heard of the fear surrounding job losses caused by more affordable AI replacements. It’s true: AI is replacing many types of jobs; however, these tend to be jobs that do not generally require a high level of thinking. As a result, it’s unlikely that artificial intelligence will entirely take over the role of software developers, but will instead be used to improve efficiency and supplement skills.
Unreliable Training Data
Because generative AI models are trained on large volumes of data, it can be difficult to ensure that all the information used is accurate and appropriate. At times, this unreliable training data can lead to inaccurate responses that are presented as fact–a phenomenon known as AI hallucination. Consequently, a software developer using artificial intelligence to generate code may encounter bugs and poorly programmed code when using AI models trained on unrefined datasets.
Accidental Copyright Infringements
Many popular generative AI models have been trained using online sources, such as articles, books, and code, without the expressed permission of copyright holders. As a result, it’s possible that some AI generated responses may inadvertently infringe upon copyright. Though unlikely, there is a chance that developers using AI generated code could be subject to legal penalties due to accidentally plagiarized code.
The Future of AI in Software Engineering
Now that you understand the benefits and challenges regarding the use of AI in software engineering, let’s discuss the lasting effects the technology will likely have on the development industry.
Increasing Adoption by Developers
As developer tools integrate AI capabilities more seamlessly, using AI will become a standard part of the development process rather than an occasional supplemental tool. Engineers will increasingly welcome AI assistants that can boost their productivity and reduce repetitive tasks.
Continued Improvements in AI Capabilities
The accuracy and versatility of AI systems will continue advancing rapidly thanks to improved algorithms and more training data. We can expect AI to become incredibly proficient at code generation, debugging, testing, and optimization in the near future. AI will also be able to write and refine far more complex and robust programs than what is currently possible.
Integration Across Software Development Methodology
Rather than being limited to specific tasks like writing code, AI will evolve into a partner that can provide value across the entire software development life cycle. AI could help craft technical specifications, provide insights during planning, oversee QA testing, monitor application performance, and even coordinate team collaboration. These various capabilities will allow AI to seamlessly integrate across development methodologies.
AI Integration From Idea Maker
Looking to integrate AI into your workflow? At Idea Maker, we offer artificial intelligence software development solutions from a team of dedicated experts. Schedule a free consultation with us today to learn more about our services.