MoCo: Fuzzing Deep Learning Libraries via Assembling Code

• URL: http://arxiv.org/abs/2405.07744v1
• Date: Mon, 13 May 2024 13:40:55 GMT
• Title: MoCo: Fuzzing Deep Learning Libraries via Assembling Code
• Authors: Pin Ji, Yang Feng, Duo Wu, Lingyue Yan, Pengling Chen, Jia Liu, Zhihong Zhao,
• Abstract summary: Deep learning techniques have been applied in software systems with various application scenarios. DL libraries serve as the underlying foundation for DL systems, and bugs in them can have unpredictable impacts. We propose MoCo, a novel fuzzing testing method for DL libraries via assembling code.
• Score: 13.937180393991616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapidly developing deep learning (DL) techniques have been applied in software systems with various application scenarios. However, they could also pose new safety threats with potentially serious consequences, especially in safety-critical domains. DL libraries serve as the underlying foundation for DL systems, and bugs in them can have unpredictable impacts that directly affect the behaviors of DL systems. Previous research on fuzzing DL libraries still has limitations in the diversity of test inputs, the construction of test oracles, and the precision of detection. In this paper, we propose MoCo, a novel fuzzing testing method for DL libraries via assembling code. MoCo first disassembles the seed code file to obtain the template and code blocks, and then employs code block mutation operators (e.g., API replacement, random generation, and boundary checking) to generate more new code blocks adapted to the template. By inserting context-appropriate code blocks into the template step by step, MoCo can generate a tree of code files with intergenerational relations. According to the derivation relations in this tree and the applied mutation operators, we construct the test oracle based on the execution state consistency. Since the granularity of code assembly and mutation is controlled rather than randomly divergent, we can quickly pinpoint the lines of code where the bugs are located and the corresponding triggering conditions. We conduct a comprehensive experiment to evaluate the efficiency and effectiveness of MoCo using three widely-used DL libraries (i.e., TensorFlow, PyTorch, and Jittor). During the experiment, MoCo detects 64 new bugs of four types in three DL libraries, where 51 bugs have been confirmed, and 13 bugs have been fixed by developers.

Related papers

• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• DLLens: Testing Deep Learning Libraries via LLM-aided Synthesis [8.779035160734523]
  Testing is a major approach to ensuring the quality of deep learning (DL) libraries. Existing testing techniques commonly adopt differential testing to relieve the need for test oracle construction. This paper introduces thatens, a novel differential testing technique for DL library testing.
  arXiv  Detail & Related papers  (2024-06-12T07:06:38Z)
• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• LLMorpheus: Mutation Testing using Large Language Models [7.312170216336085]
  This paper presents a technique where a Large Language Model (LLM) is prompted to suggest mutations by asking it what placeholders that have been inserted in source code could be replaced with. We find LLMorpheus to be capable of producing mutants that resemble existing bugs that cannot be produced by StrykerJS, a state-of-the-art mutation testing tool.
  arXiv  Detail & Related papers  (2024-04-15T17:25:14Z)
• DebugBench: Evaluating Debugging Capability of Large Language Models [80.73121177868357]
  DebugBench is a benchmark for Large Language Models (LLMs) It covers four major bug categories and 18 minor types in C++, Java, and Python. We evaluate two commercial and four open-source models in a zero-shot scenario.
  arXiv  Detail & Related papers  (2024-01-09T15:46:38Z)
• InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback [50.725076393314964]
  We introduce InterCode, a lightweight, flexible, and easy-to-use framework of interactive coding as a standard reinforcement learning environment. Our framework is language and platform agnostic, uses self-contained Docker environments to provide safe and reproducible execution. We demonstrate InterCode's viability as a testbed by evaluating multiple state-of-the-art LLMs configured with different prompting strategies.
  arXiv  Detail & Related papers  (2023-06-26T17:59:50Z)
• CONCORD: Clone-aware Contrastive Learning for Source Code [64.51161487524436]
  Self-supervised pre-training has gained traction for learning generic code representations valuable for many downstream SE tasks. We argue that it is also essential to factor in how developers code day-to-day for general-purpose representation learning. In particular, we propose CONCORD, a self-supervised, contrastive learning strategy to place benign clones closer in the representation space while moving deviants further apart.
  arXiv  Detail & Related papers  (2023-06-05T20:39:08Z)
• ACETest: Automated Constraint Extraction for Testing Deep Learning Operators [23.129431525952263]
  It is essential that the test cases pass the input validity check and are able to reach the core function logic of the operators. Existing techniques rely on either human effort or documentation of DL library APIs to extract the constraints. We propose ACETest, a technique to automatically extract input validation constraints from the code to build valid yet diverse test cases.
  arXiv  Detail & Related papers  (2023-05-29T06:49:40Z)
• MEMO: Coverage-guided Model Generation For Deep Learning Library Testing [11.263121366956726]
  A few techniques have been proposed to test deep learning (DL) libraries by generating DL models as test inputs. But the test effectiveness of these techniques is constrained by the diversity of generated DL models. We propose MEMO to efficiently generate diverse DL models by exploring layer types, layer pairs, and layer parameters.
  arXiv  Detail & Related papers  (2022-08-02T14:53:02Z)
• Fault-Aware Neural Code Rankers [64.41888054066861]
  We propose fault-aware neural code rankers that can predict the correctness of a sampled program without executing it. Our fault-aware rankers can significantly increase the pass@1 accuracy of various code generation models.
  arXiv  Detail & Related papers  (2022-06-04T22:01:05Z)
• Predicting Vulnerability In Large Codebases With Deep Code Representation [6.357681017646283]
  Software engineers write code for various modules, quite often, various types of errors get introduced. Same or similar issues/bugs, which were fixed in the past (although in different modules), tend to get introduced in production code again. We developed a novel AI-based system which uses the deep representation of Abstract Syntax Tree (AST) created from the source code and also the active feedback loop.
  arXiv  Detail & Related papers  (2020-04-24T13:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.