To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?

By Matteo Ciniselli, L. Pascarella, Gabriele Bavota. 2022

32 citations recorded by the source corpus.

Deep Learning (DL) models have been widely used to support code completion. These models, once properly trained, can take as input an incomplete code component (e.g., an incomplete function) and predict the missing tokens to finalize it. GitHub Copilot is an example of code recommender built by training a DL model on millions of open source repositories: The source code of these repositories acts as training data, allowing the model to learn “how to program”. The usage of such a code is usually regulated by Free and Open Source Software (FOSS) licenses, that establish under which conditions the licensed code can be redistributed or modified. As of Today, it is unclear whether the code generated by DL models trained on open source code should be considered as “new” or as “derivative” work, with possible implications on license infringements. In this work, we run a large-scale study investigating the extent to which DL models tend to clone code from their training set when recommending code completions. Such an exploratory study can help in assessing the magnitude of the potential licensing issues mentioned before: If these models tend to generate new code that is unseen in the training set, then licensing issues are unlikely to occur. Otherwise, a revision of these licenses urges to regulate how the code generated by these models should be treated when used, for example, in a commercial setting. Highlights from our results show that ∼10% to ∼0.1% of the predictions generated by a state-of-the-art DL-based code completion tool are Type-1 clones of instances in the training set, depending on the size of the predicted code. Long predictions are unlikely to be cloned.

Subfields: Code Generation & Program Synthesis, Software Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Code Generation & Program Synthesis

1. Paul Denny
2. Juho Leinonen
3. Sami Sarsa
4. Arto Hellas

In Software Engineering

1. Juho Leinonen
2. Arto Hellas

Citations in the corpus

• Software Testing With Large Language Models: Survey, Landscape, and Vision (2023, unknown): “• The paper focuses on testing the performance of LLMs, such as fairness, stability, security, etc. [124], [125], [126].”
• Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models (2022, background): “However, we consider this possibility very remote [12].”