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For example, Intel’s ControlFlag and Machine Inferred Code Similarity engine can autonomously detect errors in code and determine when two pieces of code perform similar tasks.

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“ would considerably decrease their working time and also could potentially reduce the computation and operational cost, as a bug might degrade the system performance or even crash the entire system.”ĬodeT5 adds to the growing list of models trained to complete software programming tasks. Software code intelligence techniques can help developers to reduce tedious repetitive workloads, enhance the programming quality and improve the overall software development productivity,” the researchers wrote in their paper. “With the goal of improving the development productivity of software with machine learning methods, software intelligence research has attracted increasing attention in both academia and industries over the last decade. To demonstrate the model’s usefulness, the researchers built an AI-powered coding assistant for Apex, Salesforce’s proprietary programming language with Java-like syntax, that lets developers type a natural language description to generate a target function or summarize a function into code comments. Codex can also be prompted to generate racist and otherwise harmful outputs as code, like the word “terrorist” and “violent” when writing code comments with the prompt “Islam.”īut the Salesforce team says that they took steps to prune and debias CodeT5, including by cleaning and filtering the training data for problematic content. OpenAI similarly found that its Codex model, which was also trained on code from open source GitHub repositories, could suggest compromised packages, invoke functions insecurely, and produce programming solutions that appear correct but don’t actually perform the intended task.

And it might produce vulnerable code that negatively affects software. Moreover, they say, CodeT5 could contain sensitive information like personal addresses and identification numbers. The Salesforce researchers acknowledge that the datasets used to train CodeT5 could encode some stereotypes like race and gender from the text comments - or even from the source code.

(Parameters are the parts of the machine learning model learned from historical training data.) The design innovations enabled it to achieve top-level performance on fourteen tasks in the CodeXGLUE benchmark, including text-to-code generation and code-to-code translation. The largest and most capable version of CodeT5, which had 220 million parameters, took 12 days to train on a cluster of 16 Nvidia A100 GPUs with 40GB of memory. Most came from the CodeSearchNet dataset - which spans Ruby, JavaScript, Go, Python, PHP, C, and C# - supplemented by two C and C# datasets from BigQuery. To train CodeT5, the team sourced over 8.35 million instances of code, including user-written comments from publicly available, open source GitHub repositories. It reframes natural language processing tasks into a unified text-to-text-format, where the input and output data are always strings of text - allowing the same model to be applied to virtually any natural language processing task. CodeT5 builds on Google’s T5 (Text-to-Text Transfer Transformer) framework, which was first detailed in a paper published in 2020.

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