AI-enabled software development is a growing trend that is having a significant impact on the software development process. However, the concept isn’t new, as code-generating techniques have been part of the IT industry for decades. The roots began to develop decades ago, as earlier technical tools made it faster and more efficient for developers to generate code.
Historically, computer-aided software engineering (CASE) tools and code generators such as Synon (a tool used to develop code for the IBMi) were in some ways precursors to AI-enabled development tools. These tools were used to build some foundational programming and functioned using types of intelligent code completion.
The adoption of AI-enabled software development has accelerated in recent years, especially when combining Generative AI tools with large language models (LLMs) and natural language processing (NLP). However, there are both advantages and limitations when considering adopting AI tools into software development.
Where AI tools are being used in software development
The use of AI-assisted tools for the development process is ramping up. Several industry studies show that a majority of developers have considered or are already using AI tools in some way for software development. They often have support from their organizations.
For instance, a 2024 study from Stack Overflow showed that 76% of all respondents are using or are planning to use AI tools in their development process this year, an increase from 70% in 2023. Meanwhile, a 2024 survey from GitHub showed that 30-40% of developers who responded indicated their organizations “actively encouraged and promoted the adoption of AI coding tools.”
AI-assisted development tools are more frequently being used in some key areas including:
- Code generation
- Bug detection and fixes
- Testing automation
- Documentation
- DevOps pipeline
- Continuous integration and continuous delivery (CI/CD) pipeline
Some highlighted examples of these areas are as follows:
Code generation
Many AI coding tools incorporate NLP for intelligent auto-completion and code suggestions. NLP can also be used for converting natural language descriptions into code.
Bug and error detection
GenAI-driven tools can automatically detect bugs, vulnerabilities or inefficiencies in the code. This works by analyzing patterns within the codebase and offering solutions. AI tools can also be used to automate testing processes.
DevOps and CI/CD pipelines
AI tools can automate tasks such as monitoring and scaling in CI/CD pipelines, which can improve build efficiency and deployment speed.
Documentation
AI can be used to quickly and efficiently automate documentation generation and maintenance, ensuring that all documentation is updated and analyzed for inconsistencies, errors or gaps. AI tools can also automate translation for documentation.
What are the benefits of AI-assisted software development
The key benefits of AI-assisted software development include the following:
- Automating and saving time on repetitive or manual tasks, thereby increasing productivity
- Opportunity to improve code quality
- Potentially accelerating the product life cycle creating a faster time-to-market
- Making certain processes in the CI/CD pipeline more efficient
- Freeing up developers’ time for more complex tasks
- Enhancing collaboration among teams
For the case of using AI tools for development, productivity is emerging as a top benefit, Github’s 2024 developer survey revealed. “Our survey respondents reported that AI helps them work more productively, using the saved time to design systems, collaborate more, and meet customer requirements better.”
Moreover, McKinsey notes that the accumulated benefits of introducing AI-driven tools to software development can drive innovation and potentially make software organizations more competitive over time.
What are the concerns – and some limitations?
Despite the potential for making gains in innovation and productivity, organizations must still consider the limitations and risks involved with using AI-driven tools.
For instance, despite the widespread interest in using AI-enabled tools, studies nonetheless show that developers have doubts about the efficacy of AI tools for development, particularly in complex cases.
A recent Stack Overflow study shows developers remain concerned about the accuracy of using AI tools to generate code. Nearly half of “professional developers believe AI tools are bad or very bad at handling complex tasks,” the survey notes. The study also reported that “72% of all respondents are favorable or very favorable of AI tools for development. This is lower than last year’s favorability of 77%; a decline in favorability could be due to disappointing results from usage.”
Another top concern of utilizing AI-assisted tools in development is data model bias, according to IBM, although they note that using “diverse, representative and unbiased training data,” regular audits of AI outputs and bias detection tools can mitigate some of this risk.
Other limitations involve problematic code composition, especially when integrating with existing codebases. A report from DevOps.com states that “AI tools excel at generating new code snippets but often lack the contextual understanding needed to properly integrate with existing codebases.”
Ultimately, it’s up to organizations to implement policies for using AI tools that not only advance efficiency and productivity but also prioritize governance and compliance. This will address risks and concerns by ensuring that the tools are used to achieve organizational goals. Strategies should include developing quality guidelines and stronger test requirements for AI-generated code, as well as comprehensive AI training and workforce upskilling for development teams.