From creating comprehensive essays to writing intriguing fiction, there’s hardly anything untouched by the impact of generative AI. The technology has caught the attention of forward-thinking engineering companies, too. Many have attempted generative AI-assisted code development and seen measurable improvements in overall efficiency. However, the potential of this disruptive technology isn’t limited to code generation. A more comprehensive approach where generative AI gets integrated across different stages of the software development lifecycle (SDLC) can open doors to many untapped opportunities.
So, how exactly does generative AI fast-track software development? And what are the possible risks and challenges that arise from using this nascent technology? Let’s find out.
Opening a plethora of possibilities
Generative AI systems learn to generate new content by extrapolating from the training data. Models trained in massive amounts of functional, high-quality code become acquainted with the grammar and syntax of a programming language. As a result, they start predicting the next command in a code accurately. When this accuracy reaches acceptable levels, these generative AI systems are deployed to generate new code based on input parameters or ‘prompts’ provided by a user. Models that have been trained on large volumes of proprietary code begin to understand the specific needs of a development project or organization. As a result, they are able to produce highly customized output that is grounded in a particular context.
Given most development teams spend the majority of their time writing and testing code, using generative AI tools results in up to a 30% boost in developer productivity. Recent research by GitHub suggests that these productivity gains could inject over $1.5 trillion into global GDP. We are currently on the cusp of a massive transformation; the demand for high-quality software solutions will only grow with time. This surge in demand will cause more developers to explore generative AI tools to speed up development cycles. Over time, these shifts could have an enormous impact on the global economy.
Essential use cases of generative AI in software development
Present-day organizations need to develop custom software to handle various aspects of their operations or to solve customer challenges. But creating a high-performance software solution is no easy undertaking. It requires time, cost, and other resources. In most organizations, internal tech teams are overburdened with a backlog of projects. Interviewing and hiring new talent is also time and resource-intensive. In such a scenario, generative artificial intelligence allows teams to convert their ideas to tangible solutions quickly and cost-efficiently while meeting the requirements of their employees and customers.
Here are a few areas in the SDLC where generative AI-based solutions can be applied successfully.
- Code generation: Efficiently creating code is one of the most obvious use cases of generative AI. Existing Generative AI systems are a work in progress and are incapable of generating the entire codebase for any application. Yet, they can produce boilerplate code that can be reused across multiple projects or modules. Generative AI can also create code snippets that users can add to other modules. As a result, much repetitive work gets eliminated.
- Bug detection: Now, one may ask: how exactly will generative AI improve debugging when we already have an arsenal of tools available for just this task? While the existing bug detection tools thoroughly review the source code for defects, generative AI can compile debugging reports from these tools. This would help engineering teams understand the pattern of errors and take steps to prevent them. They can also get recommendations on how to fix these errors and improve the overall code quality.
- Codebase documentation: Documentation provides context to those working on the code. It helps developers understand why a specific module, library, or API was used while writing the code. Generative AI systems can assist by summarizing code functionalities and explaining what a piece of code does. These insights are then compiled into easy-to-understand documentation.
- Code optimization: An essential part of development is code optimization, where certain parts of the existing code are restructured to improve performance. It becomes indispensable when a section of the code runs slowly or consumes more resources than necessary. Generative AI proves useful here. Users can feed existing codes into generative AI tools to identify inefficient portions and create newer, faster versions without changing the underlying functionality.
Potential challenges: How AI may impact code quality
There’s no doubt that engineering companies are increasingly relying on generative AI-based solutions to write and debug code, refactor existing code, and simplify documentation. However, as generative AI is adopted into software development, IT decision-makers must evaluate the tradeoff between speed and quality inherent to its use.
The technology abstracts developmental complexities through automated code generation or optimization. But, there is always a critical risk of oversimplifying intricate technical components. Overlooking this risk may lead to technical debt, systemic vulnerabilities, or misalignment with long-term security requirements.
Let’s talk about the ways in which generative AI may affect code quality and how these issues can be addressed.
Code accuracy: With generative AI tools, balancing the speed of code generation with accuracy (functional correctness, security, readability) becomes a challenge. AI-generated code may include logical flaws that appear syntactically valid but fail under edge cases or when the software scales. Generative AI models may also falter when handling intricate logic or domain-specific requirements. That’s why their outputs often require rigorous validation, particularly as the complexity of prompts increases.
To address these shortcomings, engineering teams must integrate human-in-the-loop validation into their code review processes. As humans don’t need to write every single line of code now, they can shift their focus on four critical areas that warrant attention:
- Auditing the code rigorously to ensure logical integrity and compliance adherence
- Creating intricate, domain-specific prompts to generate code that meets functional and non-functional requirements
- Mentoring junior developers on how to evaluate and optimize AI-generated output
- Collaborating with a generative AI software company to spot and resolve issues related to code accuracy
Prompt standardization: The quality of code generated by generative AI systems depends largely on the prompts provided by the user. In most organizations, there is a dearth of skilled prompt engineers, which leads to inconsistent, non-standardized inputs. Additionally, users lacking specialized knowledge interact with these systems, which may result in unexpected outputs.
Engineering teams can address the issue by creating a prompt engineering center of excellence (CoE). The CoE would be responsible for building a library of tested, reusable prompts that ensure the code generated by the AI tools is consistent and scalable. Also, complex development tasks can be broken down into smaller, more manageable prompts to ensure easy testing and integration of the generated code. Plus, since prompt engineering is an emerging discipline, organizations should invest in training their teams in the fundamentals of prompt design.
Innovation-related barriers: Generative AI’s reliance on historical training data limits its ability to spur innovation in software development. Generative AI models are trained on existing code repositories, libraries, and frameworks. As a result, their outputs are inherently constrained by patterns, practices, and technologies that already exist. Generative AI cannot invent novel algorithms beyond its training scope. Many times, it prioritizes frequently used, “safe” code snippets and overlooks niche or cutting-edge techniques. It may also struggle with domain-specific or proprietary logic unique to an organization, as these are rarely present in public training datasets.
To overcome such hurdles, technology teams must train generative AI systems to understand everyday language and organization-specific terms. This will help AI think within the organization’s unique context, making its outputs more useful and accurate. Additionally, teams should continuously feed AI new data (e.g., industry trends and user feedback) to keep the model relevant and up to date.
The final word
Generative AI offers significant advantages in software development by accelerating coding tasks and streamlining time-consuming work. However, its dependence on historical data and variable output quality raises questions about its reliability and usability. To make sure they get the most out of these generative AI systems, teams must balance AI automation with human oversight.
Rigorous code reviews, standardized prompts, and continuous training ensure the generated code is accurate and aligned with project goals. While AI handles repetitive work, human creativity remains vital for solving unique challenges and building innovative software solutions. By improving collaboration between AI tools and developer expertise, organizations can maintain high-quality standards while embracing efficiency. Responsible adoption of these tools will shape the future of software development.
