# DeepBugs: A Learning Approach to Name-based Bug Detection

arXiv: https://arxiv.org/abs/1805.11683 · HTML: https://mlsanity.com/paper/1805.11683/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2018.

## Built on by (most cited first, 30 of 65)

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- [Self-Supervised Bug Detection and Repair](https://mlsanity.com/paper/2105.12787/) (2021)
- [On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages](https://mlsanity.com/paper/2204.09653/) (2022)
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- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [Generating Adversarial Computer Programs using Optimized Obfuscations](https://mlsanity.com/paper/2103.11882/) (2021)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code](https://mlsanity.com/paper/2205.11739/) (2022)
- [MdEval: Massively Multilingual Code Debugging](https://mlsanity.com/paper/2411.02310/) (2024)
- [On Distribution Shift in Learning-based Bug Detectors](https://mlsanity.com/paper/2204.10049/) (2022)
- [AEON: A Method for Automatic Evaluation of NLP Test Cases](https://mlsanity.com/paper/2205.06439/) (2022)
- [Learning to Reduce False Positives in Analytic Bug Detectors](https://mlsanity.com/paper/2203.09907/) (2022)
- [An Exploratory Study on Code Attention in BERT](https://mlsanity.com/paper/2204.10200/) (2022)
- [DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning](https://mlsanity.com/paper/2108.08041/) (2021)
- [VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning](https://mlsanity.com/paper/2112.02650/) (2021)
- [Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions](https://mlsanity.com/paper/2203.03771/) (2022)
- [A Contemporary Survey of Large Language Model Assisted Program Analysis](https://mlsanity.com/paper/2502.18474/) (2025)
- [Improving Automated Program Repair with Domain Adaptation](https://mlsanity.com/paper/2212.11414/) (2022)
- [kTrans: Knowledge-Aware Transformer for Binary Code Embedding](https://mlsanity.com/paper/2308.12659/) (2023)
- [SYNFIX: Automatically Fixing Syntax Errors using Compiler Diagnostics](https://mlsanity.com/paper/2104.14671/) (2021)
- [LExecutor: Learning-Guided Execution](https://mlsanity.com/paper/2302.02343/) (2023)
- [FLAG: Finding Line Anomalies (in code) with Generative AI](https://mlsanity.com/paper/2306.12643/) (2023)
- [DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2402.13291/) (2024)
- [An Empirical Study on Capability of Large Language Models in Understanding Code Semantics](https://mlsanity.com/paper/2407.03611/) (2024)
- [MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning](https://mlsanity.com/paper/2206.06460/) (2022)
- [Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation](https://mlsanity.com/paper/2109.03341/) (2021)

## Abstract (arXiv)

Natural language elements in source code, e.g., the names of variables and functions, convey useful information. However, most existing bug detection tools ignore this information and therefore miss some classes of bugs. The few existing name-based bug detection approaches reason about names on a syntactic level and rely on manually designed and tuned algorithms to detect bugs. This paper presents DeepBugs, a learning approach to name-based bug detection, which reasons about names based on a semantic representation and which automatically learns bug detectors instead of manually writing them. We formulate bug detection as a binary classification problem and train a classifier that distinguishes correct from incorrect code. To address the challenge that effectively learning a bug detector requires examples of both correct and incorrect code, we create likely incorrect code examples from an existing corpus of code through simple code transformations. A novel insight learned from our work is that learning from artificially seeded bugs yields bug detectors that are effective at finding bugs in real-world code. We implement our idea into a framework for learning-based and name-based bug detection. Three bug detectors built on top of the framework detect accidentally swapped function arguments, incorrect binary operators, and incorrect operands in binary operations. Applying the approach to a corpus of 150,000 JavaScript files yields bug detectors that have a high accuracy (between 89% and 95%), are very efficient (less than 20 milliseconds per analyzed file), and reveal 102 programming mistakes (with 68% true positive rate) in real-world code.