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A collection of over 250 uses for artificial intelligence

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Code generation

Introduction

The IT industry is going through a major shift at the present time and this is due to the growing use of Artificial Intelligence (AI). One of the most emerging fields where AI is being widely used is in the area of code generation. Code generation is an essential part of the software development process and is defined as the process of creating source code that is more effective, secure, and robust than its hand-written counterpart. Generative AI (also known as AI-powered writing tools) has played a very crucial role in the automation of this process thus changing the face of the IT industry. Some of the most advanced AI systems that are capable of learning, cognition and in some cases, reasoning are being used in generating code with the aim of minimizing mistakes that are made by developers as well as to increase efficiency. This article focuses on the application of Gen AI in code generation in the context of the IT industry and discusses the problems, the AI solutions, the advantages, ROI and concrete applications.

Challenges

There are several challenges that the process of code generation has to deal with. Among them, we can mention the high costs of employing skilled developers, the effort that goes into writing the code by hand, and the mistakes that a human being can make and which may result in the appearance of bugs. Also, there is a problem of how to ensure that the code that is written meets the requirements of all the parts of a software project and that it is of high quality. Other challenges are; how to integrate and handle legacy systems and how difficult it is to learn and understand new languages and frameworks.

AI Solutions

This is where Gen AI comes in as a strong solution to these problems. There are AI-powered tools like Kite, Codota, and Tabnine that incorporate Machine Learning/ Natural Language Processing to help in code generation process. They offer contextual auto completes to help developers code faster and with less chances of mistakes. Another such AI-based tool is DeepCode which uses GitHub project data to provide real-time feedback on potential coding mistakes. OpenAI’s GPT-3, which is considered to be one of the most advanced language prediction model, is also being experimented with for code generation and has produced quite encouraging outcomes. These AI solutions have the potential to change the approach to developing and reviewing code, thus creating a new paradigm of what can be called ‘co-development’ between human and AI.

Benefits

There is a long list of benefits of using Gen AI in code generation. It also decreases the time that developers spend on menial coding tasks thus enabling the developers to work on other important and exciting aspects of software development. This is because AI does not make mistakes that are common to human beings thus outputting cleaner and more efficient code hence reducing on time and resources needed for testing and debugging. In addition, AI code generation tools can also support the uniformity of a project as a good practice in coding will be followed. They may also support refactoring of legacy applications and are especially useful for teaching entry-level developers how to use new technologies.

Return on Investment

There are numerous benefits that can be derived from the use of Gen AI in code generation and the ROI can be quite impressive. This is because by performing basic coding functions, a lot of time and resources that would have been used in coding and testing can be saved. Also, AI can generate better codes which results to development of better software that is less likely to develop faults thus requiring little or no maintenance. According to a study by Boston Consulting Group, companies that have adopted AI have seen an increase in productivity by 17% and reduction in costs by 14% on average. Although, there are high costs that are required to invest in AI, the future returns and cost reductions make it worthwhile.

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