Machine learning-based program analysis using synthetically generated labeled data
US-11593675-B1 · Feb 28, 2023 · US
Automated program repair tool
US2024370352A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2024370352-A1 |
| Application number | US-202418628773-A |
| Country | US |
| Kind code | A1 |
| Filing date | Apr 7, 2024 |
| Priority date | May 15, 2020 |
| Publication date | Nov 7, 2024 |
| Grant date | — |
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Abstract
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An automated program repair tool utilizes a neural transformer model with attention to predict the contents of a bug repair in the context of source code having a bug of an identified bug type. The neural transformer model is trained on a large unsupervised corpus of source code using a span-masking denoising optimization objective, and fine-tuned on a large supervised dataset of triplets containing a bug-type annotation, software bug, and repair. The bug-type annotation is derived from an interprocedural static code analyzer. A bug type edit centroid is computed for each bug type and used in the inference decoding phase to generate the bug repair.
First claim
Opening claim text (preview).
What is claimed: 1 . A system comprising: a processor; and a memory that stores a program configured to be executed by the processor; wherein the program comprises instructions to perform actions that: obtain a source code snippet with a software bug associated with a bug type; obtain a bug-type edit centroid for the bug type; access a neural transformer model with attention having at least one edit encoder block, at least one context encoder block, and at least one decoder block; and perform a beam search to generate a repaired source code snippet for the source code snippet with the software bug, wherein the beam search invokes the neural transformer model with attention given the bug-type edit centroid and the source code snippet with the software bug to predict each token of the repaired source code snippet autoregressively, wherein the at least one edit encoder block generates an edit embedding for the bug-type edit centroid, wherein the at least one context encoder generates a context embedding for the source code snippet with the software bug, wherein the at least one decoder block generates an output probability distribution given the edit embedding and the context embedding, wherein the output probability distribution associates a probability of a token following a preceding sequence of tokens.
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Classifications
Transfer learning · CPC title
Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title
Supervised learning · CPC title
Auto-encoder networks; Encoder-decoder networks · CPC title
Machine learning · CPC title
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- What does patent US2024370352A1 cover?
- An automated program repair tool utilizes a neural transformer model with attention to predict the contents of a bug repair in the context of source code having a bug of an identified bug type. The neural transformer model is trained on a large unsupervised corpus of source code using a span-masking denoising optimization objective, and fine-tuned on a large supervised dataset of triplets conta…
- Who is the assignee on this patent?
- Microsoft Technology Licensing Llc
- What technology area does this patent fall under?
- Primary CPC classification G06F11/362. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Thu Nov 07 2024 00:00:00 GMT+0000 (Coordinated Universal Time) (A1). Legal status and post-grant events are not shown on this page.
- What related patents are in patentsdb?
- We list 3 related publications on this page (citations in our corpus or others sharing the same primary CPC).