By Dr Leslie Kanthan, CEO and Co-founder at TurinTech
Generative AI, as demonstrated by ChatGPT, has triggered numerous discussions within the AI industry regarding its emerging role and capabilities. Many questions have arisen, such as the accuracy of generative AI results, whether it will replace human input, and the ethical considerations associated with using AI for content generation. However, due to the nascent stage of generative AI, it remains challenging to provide conclusive answers to most of these inquiries.
Despite being a relatively new technology, generative AI tools have already demonstrated impressive capabilities and offer promising prospects for the future. This article delves into the landscape of generative AI, with a particular focus on its role in the software and coding domain.
What is generative AI?
Traditionally, machine learning and artificial intelligence have been utilised for analytical purposes, whereby algorithms analyse data and generate insights, often at a scale and speed that surpasses human capacity.
Generative AI algorithms utilise data to produce novel and distinctive content, including but not limited to text, images, videos, and code, giving rise to its moniker “generative AI.”
Generative AI has a vast array of applications, ranging from creative industries to areas like banking and healthcare. These industries can reap various benefits from generative AI, with improved prediction accuracy and efficiency being some of the most notable advantages.
The types of tasks that generative AI can achieve in different application spaces are also rather varied. These include activities such as text generation and the creation of audio and video material.
Using AI to generate production-ready code
Generative AI has the potential to aid software development by producing and optimising code bases, leading to enhanced application efficiency and quality. Various tools exist that contribute to different aspects of the code development process, which is categorised based on three functions:
- Code generation (tools that take natural language input and convert it to code)
- Code completion (tools that complete sections of code once provided with a prompt)
- Code review (tools that test code for quality, to ensure that code efficiently does what it is expected to do)
Although code production and testing tools hold the potential to revolutionise the coding industry, it remains important to maximise the benefits of generative tools. Through optimisation, the outputs of generative tools can be fine-tuned for faster performance and lower cost, yielding optimal outcomes for businesses.
With the increasing complexity of AI, the underlying code is also becoming more intricate. Generative AI has made a significant contribution in streamlining the coding process by enabling the conversion of natural language prompts into code. This not only enhances the efficiency of coding for developers but also opens up opportunities for individuals with limited formal training or background to participate in coding. Ideally, anyone with an interest in programming can input natural language commands and have these tools generate relevant code.
Often, code completion platforms offer an extension to be integrated into a developer’s coding environment, allowing for seamless integration of generative AI into the coding workflow.
This is a process that involves using AI-based tools to generate suggestions for code. These tools employ natural language processing to analyse code, suggest relevant code snippets or keywords, and complete partial code with the appropriate syntax. As they simplify the writing of complex code, generative AI tools for code completion are gaining popularity among developers.
Code assessment for bugs and inefficiencies is just as crucial as code development itself. Consequently, code reviews are an integral component of the software development process and can be conducted either manually or with the assistance of tools. AI-powered tools have revolutionised the traditional code review process by providing superior insights and enhanced efficiency.
To attain production-quality standards, AI-generated code necessitates optimisation, despite the rapid advancement in code generation, completion, and review technologies. The following are critical features that code should possess:
Reliability: One of the essential traits of production-quality code is reliability, which refers to the ability of the code to perform its intended function without significant errors.
Clarity and consistency: Another crucial aspect of production-quality code is clarity, which means that the code is easy to comprehend and adheres to consistent stylistic and standard conventions.
Robust: The code’s ability to handle diverse application scenarios, including rare occurrences, is another vital aspect of production-quality code.
Efficient: Efficiency is also a critical attribute of production-quality code. The code must be productive, and should utilise minimal time, energy, and resources to execute its intended function.
Within the present generative AI framework, the code generated by generative tools often falls short of meeting these quality standards and may even contain errors. In fact, some generative AI tools have produced faulty code, such as Codex, with researchers finding it is more likely to write incorrect and “insecure” (in the cybersecurity sense) solutions to programming problems compared to a control group. This underscores the importance of having supplementary tools that can assess the quality of code generated using generative technologies. This is also why optimising the code generated by generative AI tools is also important, because it can improve the performance and efficiency of the code, resulting in faster and more reliable software.
In other words:
Most modern-day devices and writing applications come with an autocomplete function that suggests text to the user. However, it is uncommon for users to share an autocomplete text such as an email or a text message without verifying its accuracy
Like with text autocomplete, generative AI can accelerate the development process, but deploying generated code without proper quality checks can lead to errors. In addition, the quality requirement of code is greater than text, because text can be vague, but code needs to be very specific to be executed in the expected way.
Optimising code has historically been a challenge for developers, as it can be a time-consuming and inefficient process to retrieve and improve underperforming code. Even seasoned engineers may spend several days seeking out the most effective optimisation techniques. As a result of these complexities, optimising code may often be neglected during the development process. Despite the benefits of generative AI in streamlining the code creation process, the need for optimisation remains crucial
To ensure efficient execution of code and reduce associated costs, optimisation is a critical requirement. Generative AI can play a vital role in streamlining this process and minimising developer involvement.
The bottom line
Overall, generative AI has become a popular tool in various industries, including software development, due to its ability to produce novel and distinctive content, including code. It offers numerous benefits, such as improved prediction accuracy and efficiency, and has the potential to revolutionise the coding industry. This being said, the outputs generated by these tools require optimisation to meet production-quality standards. Therefore, it is crucial to have supplementary tools that can assess the quality of code generated using generative technologies.