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A collection of over 250 uses for artificial intelligence

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Code scanning for adherence or best practices

Introduction

In the current world where IT is constantly developing and changing, AI has become a game changer that revolutionizes the industry and changes its landscape. The term Gen AI, which is short for Generative Artificial Intelligence, refers to a class of AI that has the capability of generative intelligence that means the AI has the ability to create, understand and apply concepts. One of the most promising areas where Gen AI can be applied is in code scanning for best practices in the IT field. Code scanning is an essential part of the software development process as it helps in detecting issues such as vulnerabilities, bugs and violations of coding guidelines. Gen AI can help in making this process more efficient thus improving the code quality and the overall productivity of the IT industry.

Challenges

The use of code scanning in the IT industry has some difficulties as well. First, the manual code scanning is a long process and can be inaccurate as it is done by humans. It also needs highly skilled workers, and therefore makes it difficult to find skilled workers. Second, conventional code scanners raise false positives which create unnecessary workload. In addition, they may fail to identify some of the vulnerabilities since their rule based systems have limitations. Finally, as the codebase grows and becomes more complex, it becomes harder to ensure that the best practices are being followed. The industry requires a tool that can meet these demands and at the same time be accurate and efficient.

AI Solutions

To these challenges, Gen AI presents viable solutions. This is so because Gen AI can learn and therefore perform the task more efficiently than manual methods when it comes to handling large code bases. It applies machine learning algorithms to analyse code, detect potential issues, and recommend solutions. The output from Gen AI can also be made less likely to contain false positives and false negatives since the model will be improving with every past scan and user feedback. For instance, DeepCode, a startup that was recently bought by Snyk, applies Gen AI to give real-time feedback on code quality. Another one is Embold, a tool that employs AI to break down and graphically demonstrate codebases in order to enable developers to comprehend the structure of the system and its connections.

Benefits

There are many benefits of using Gen AI in code scanning. It increases efficiency of code scanning while at the same time increasing the accuracy of the process. It also improves code quality since it is able to help in pointing out and even suggest possible solutions to potential problems that may be found in the code at the initial stages of the development process. In addition, Gen AI can assist in enforcing coding standards and best practices and thus result in better software quality in terms of robustness, security, and maintainability. Also, it can assist in the promotion of code review because it provides facts and feedback that anyone can learn from including junior developers.

Return on Investment

The ROI of applying Gen AI in code scanning is bestreemed. It decreases the effort and the time required for code review and thus the time it takes to complete a development cycle. It also helps in reducing the costs of bug fixing in the later stages of development since it is cheaper to do so by 100 times as stated by the Systems Sciences Institute at IBM. Also, the enhancement in code quality can enhance software reliability and thus decrease the costs of down time and customer service. Some real-life examples include Microsoft which claimed to have reduced the costs of code reviews to $4. 5 million per year through automation and Google that uses AI to identify 70% of its code defects.

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