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engineering, software

Replaced By A Room Full of CodeMonkeys: AI Use Cases for Software Engineers

Laura Cowan

By Laura Cowan

Laura K. Cowan is a tech editor and journalist whose work has focused on promoting sustainability initiatives for automotive, green tech, and conscious living media outlets.

ChatGPT-3 Open AI

Engineers may be grappling with the idea that AI can code, threatening low-level software developer jobs in the coming years, but AI is also a valuable tool that engineers are harnessing to do mundane tasks and process more information than humans can.

Could AI Replace Basic Software Engineering Job Functions by 2040?

According to the U.S. Department of Energy’s Oak Ridge National Laboratory, there’s a good chance that AI will replace software developers as early as 2040.

“Programming trends suggest that software development will undergo a radical change in the future: the combination of machine learning, artificial intelligence, natural language processing, and code generation technologies will improve in such a way that machines, instead of humans, will write most of their own code by 2040,” state the researchers.

Nearly 30 percent of 550 software developers surveyed by Evans Data Corporation (a California-based market research firm that specializes in software development) believe that their software development job functions will be replaced by artificial intelligence in the foreseeable future.

Janel Garvin, CEO of Evans Data, said the fear of obsolescence due to AI “was also more threatening than becoming old without a pension, being stifled at work by bad management, or by seeing their skills and tools become irrelevant.”

Relevant job skills in the age of AI include leadership, strategy, analytics skills, and user insights, as well as good old experience. Yes, AI can write and compile code, but it also can be taught to do any number of things you don't want to do as an engineer. Here are some use cases of AI for engineers, so you can find ways to make the robots work for you rather than be our overlords. Indeed says that the most in-demand AI jobs right now are data scientist, software engineer and machine learning engineer, suggesting that the jobs will continue into the near future, but the skills involved might become more layered and complex.

AI Can Compile User Insights and Analyze Code

Take user insights, for example. AI can compile user insights and analyze user behavior on chatbot tools that interact with customers. AI can also be used to estimate costs based on user behavior, something too complex for humans to process.

Microsoft's DeepCoder, built by Microsoft and the University of Cambridge, can create a new application by predicting which properties the application needs to have to generate desired outputs from set inputs.

Ubisoft's Commit Assistant AI, developed in partnership with a Concordia University researcher, automatically identifies coding defects as programmers write them, saving developers 20 percent of their time. The tool was already used to create the Rainbow Six and Assassin’s Creed games.

“It touches all software developers. I believe that in the future we will be deploying more and more AI technologies to reduce the maintenance burden in software industries,” says Concordia University researcher Wahab Hamou-Lhadj.

sample code analysis from ChatGPT

AI Programming Assistants Compile Code and Test Software

IEEE Society says that OpenAI's ChatGPT-3 will revolutionize the function of software engineers over the coming years. AI can already use AI-managed code compilers to help convert software code to machine language. Glow or Compiler.ai can be used as a backend for high-level ML frameworks, which enable code generation and optimization of neural network graphs. This improves the speed and quality of code compilation in a way that's hard to argue.

AI programming assistants are already popular among software developers, because they can assist developers with code debugging and compilation and code-driven testing. Kite and Codota are AI-based programming assistants that allow developers to write code in multiple programming languages. These tools can use libraries and complete code lines and fix syntax. Machine learning is used to write code with less typing.

AI can test software, too. Artificial intelligence enhances accuracy of the software testing process. Automated testing programs can identify bugs and create reports automatically. Eggplant and Test Sigma are two popular AI-leveraged software testing tools already in use, which help software testers write and execute automated tests to mitigate bugs and improve efficiency.

AI Detects Security Vulnerabilities

Security is one of the most important use cases for AI, as it can constantly monitor for vulnerabilities from code to exposure of assets online. Software often collects sensitive data, and AI can apply business logic to process and protect this information or keep it secure from prying eyes.

AI is already being used across the world to detect anomalous behavior online from typical human behavior. AI systems can be used to detect malware, analyze pattern recognition, and protect against malware before it accesses systms.

Automating DevOps with AI

AI can assist developers in automating updates on software to keep compliant with the latest standards of their industry. Machine learning and AI can help prevent issues deploying application upgrades and improve the success rate of deployments. The technology can also be used to prevent process bottlenecks or suggest solutions to developers running DevOps.

GPT-3 Text Embeddings and Multi-modal Chain-of-Thought Reasoning in Language Models

Want to see AI in action in engineering? Check out the following two use cases:

OpenAI GPT-3 Text Embeddings - Really a new state-of-the-art in dense text embeddings?

Multimodal Chain-of-Thought Reasoning in Language Models

ai, ai code analysis tools, ai code compilation tools, ai for software development, ai replace software engineers, ai security testing, ai software analysis tools, ai software testing, ai use cases engineering, ai user insight tools, automating devops, chatgpt 3, machine learning language models

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