Much buzz surrounds the adoption of AI-based code generation platforms. From limitations in accuracy to ethical concerns that hint towards the obsoletion of human developers, AI-based code generation has attracted the opinions of many today. In this article, we address:
Within this content, we’ll also address the burning question that most have when it comes to the adoption of AI-based code builders – will this completely replace programmers? Read on to know more!
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AI-based code generation builds code by heeding manual prompts from a developer or software tester. It can also review and correct existing code, while translating any existing code into another programming language. With intelligent technologies such as generative AI taking centre stage in today’s digital landscape, AI-based code generation has made its way into the SaaS world as well.
Code Generation as a Service (CGaaS) is an up and coming software category that is now home to numerous software companies that are part of a growing B2B SaaS market, thereby rendering code generation as a field that has potential to gain more traction, in the not-so-distant future.
Generating code (usually from scratch) is undoubtedly the biggest use case for code generation platforms. Developers deliver relevant prompts, in order to generate code that is aligned to end objectives. Some trial and error may be involved during prompting, which may indicate that the code generation platform could benefit from some improvement by ‘prompt tuning’ in order to serve results that are reasonably expected by your team.
Code generation platforms can suggest succeeding items of code, while a developer is typing. This is meant to serve as an aid, so that developers can build code faster, autonomously, and without having to toggle between multiple platforms to ensure that their work is accurate.
Code generation platforms can review existing code to check for bugs and even security loopholes. As a result, these tools can also assist software testers during their routine quality assurance checks. Vulnerability scanning is another subsequent use case, thereby assisting your teams from a cyber security standpoint too.
Code generation platforms can translate code from one programming language to another. Thanks to being trained with Large Language Models (LLMs) and deep learning, this enables code generation platforms to understand syntax across different programming languages and translate code accordingly.
Legacy code is part of a software development team’s technical debt, and neglecting any legacy code could lead to issues further down the line. From security gaps to inflexible code that cannot be integrated with new applications, legacy code can hamper the overall workability quotient of your application. Code generation platforms can transform legacy code with a small to moderate amount of manual intervention, thereby streamlining workloads for inundated development teams.
Technical debt is something that often gets sidelined as developers are constantly under pressure to work on tasks that require their immediate attention, so utilising a code generation platform to transform legacy code can significantly reduce workloads, only requiring manual reviews at best on the part of your development team.
The biggest drawback and simultaneous limitation of AI-based code builders is its lack of accuracy. Code that’s written by AI usually has numerous errors, and therefore requires much improvement at the hands of a human developer. As a result, AI written code alone cannot be relied on for coding purposes. Instead, it can be used as an aid for developers, giving them intelligent snippet suggestions as they write code, or use the first draft as a base which then needs to be thoroughly iterated before it is ever applied.
The generation of code by AI has made many wonder: will AI replace programmers? In spite of its varied use cases and perks, the limitation of accuracy alone ascertains that the answer to this question is not in the affirmative. Manual intervention shall still constitute the lion’s share of work even with the adoption of AI-based coding, within software development teams.
The answer to this question mainly depends on any bottlenecks that exist, which your software development team has been struggling to resolve. While your developers will continue to lead all coding efforts, an AI-based code builder may be useful if your teams are constrained in terms of reviewing, translating and transforming legacy code.
An optimum skill set is a must for any software development project, irrespective of whether you adopt AI-based code generation. AI isn’t a replacement for skills that a programmer brings to your team. As a result, any developer can use AI-based code generation platforms to assist them for their daily duties, while embracing workloads from other duties that may have been otherwise sidelined, such as revamping legacy code.
Ultimately, there’s no strategy to implementing AI-based coding; considering its limitations in accuracy, any developer can run it on the side to supplement their efforts, while being cognizant of the fact that they’ll still need to thoroughly assess what’s been generated, to determine suitability.
AI-based code generation offers much utility value in spite of having lower rates of accuracy, as generated code can still assist developers in building a first draft, saving them time and improving their productivity. Also executing code review capabilities that can help detect security loopholes and assist software testers during their inspection checks, AI-based code generation has multiple use cases that can be applied towards helping software development teams save time and effort.
Additionally, code builders powered by AI are very resourceful for addressing legacy code. For older codebases that are written in a language that many aren’t familiar with today, AI-based code builders can rewrite this code in line with modern programming languages, maintaining it to be more integrable with other codebases – while also alleviating any of the security vulnerabilities that are conducive to older codebases.
Contrary to the excitement and buzz that revolves around anything that is AI-powered, code generation platforms that are driven by AI, machine learning, LLM and NLP technologies serve more of a supplementary role for developers – instead of completely supplanting them across what they do.
Requiring significant manual intervention from a human developer, code generated by AI, while having limitations in terms of accuracy, can still function as an assistant by making intelligent snippet suggestions as developers type code, so time, effort and even money (in some cases) can be saved in due course of fast-paced software development.
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