DiffSpec: Differential Testing with LLMs using Natural Language Specifications and Code Artifacts

• URL: http://arxiv.org/abs/2410.04249v2
• Date: Wed, 23 Oct 2024 09:46:43 GMT
• Title: DiffSpec: Differential Testing with LLMs using Natural Language Specifications and Code Artifacts
• Authors: Nikitha Rao, Elizabeth Gilbert, Tahina Ramananandro, Nikhil Swamy, Claire Le Goues, Sarah Fakhoury,
• Abstract summary: We introduce DiffSpec, a framework for generating differential tests with large language models. We demonstrate the efficacy of DiffSpec on two different systems, namely, eBPF runtimes and Wasm validators.
• Score: 6.222103009114668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differential testing can be an effective way to find bugs in software systems with multiple implementations that conform to the same specification, like compilers, network protocol parsers, and language runtimes. Specifications for such systems are often standardized in natural language documents, like Instruction Set Architecture (ISA) specifications, Wasm specifications or IETF RFC's. Large Language Models (LLMs) have demonstrated potential in both generating tests and handling large volumes of natural language text, making them well-suited for utilizing artifacts like specification documents, bug reports, and code implementations. In this work, we leverage natural language and code artifacts to guide LLMs to generate targeted, meaningful tests that highlight meaningful behavioral differences between implementations, including those corresponding to bugs. We introduce DiffSpec, a framework for generating differential tests with LLMs using prompt chaining. We demonstrate the efficacy of DiffSpec on two different systems, namely, eBPF runtimes and Wasm validators. Using DiffSpec, we generated 359 differentiating tests, uncovering at least four distinct and confirmed bugs in eBPF, including a kernel memory leak, inconsistent behavior in jump instructions, and undefined behavior when using the stack pointer. We also found 279 differentiating tests in Wasm validators, that point to at least 2 confirmed and fixed bugs in Wizard Engine.

Related papers

• HyClone: Bridging LLM Understanding and Dynamic Execution for Semantic Code Clone Detection [3.2167919219391474]
  Code clone detection is a critical task in software engineering, aimed at identifying duplicated or similar code fragments within or across software systems.<n>Recent advances in large language models (LLMs) have shown promise in understanding code semantics.<n>We propose a novel two-stage framework that combines LLM-based screening with execution-based validation for detecting semantic clones in Python programs.
  arXiv  Detail & Related papers  (2025-08-02T13:11:56Z)
• IFEvalCode: Controlled Code Generation [69.28317223249358]
  The paper introduces forward and backward constraints generation to improve the instruction-following capabilities of Code LLMs.<n>The authors present IFEvalCode, a multilingual benchmark comprising 1.6K test samples across seven programming languages.
  arXiv  Detail & Related papers  (2025-07-30T08:08:48Z)
• BehaviorBox: Automated Discovery of Fine-Grained Performance Differences Between Language Models [55.2480439325792]
  We propose methodology for automated comparison of language models that uses performance-aware contextual embeddings to find fine-grained features of text where one LM outperforms another.<n>Our method, which we name BehaviorBox, extracts coherent features that demonstrate differences with respect to the ease of generation between two LMs.<n>We apply BehaviorBox to compare models that vary in size, model family, and post-training, and enumerate insights into specific contexts that illustrate meaningful differences in performance which cannot be found by measures such as corpus-level perplexity alone.
  arXiv  Detail & Related papers  (2025-06-02T19:44:06Z)
• CLOVER: A Test Case Generation Benchmark with Coverage, Long-Context, and Verification [71.34070740261072]
  This paper presents a benchmark, CLOVER, to evaluate models' capabilities in generating and completing test cases. The benchmark is containerized for code execution across tasks, and we will release the code, data, and construction methodologies.
  arXiv  Detail & Related papers  (2025-02-12T21:42:56Z)
• LlamaRestTest: Effective REST API Testing with Small Language Models [50.058600784556816]
  We present LlamaRestTest, a novel approach that employs two custom Large Language Models (LLMs) to generate realistic test inputs. We evaluate it against several state-of-the-art REST API testing tools, including RESTGPT, a GPT-powered specification-enhancement tool. Our study shows that small language models can perform as well as, or better than, large language models in REST API testing.
  arXiv  Detail & Related papers  (2025-01-15T05:51:20Z)
• Commit0: Library Generation from Scratch [77.38414688148006]
  Commit0 is a benchmark that challenges AI agents to write libraries from scratch. Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests. Commit0 also offers an interactive environment where models receive static analysis and execution feedback on the code they generate.
  arXiv  Detail & Related papers  (2024-12-02T18:11:30Z)
• SpecTool: A Benchmark for Characterizing Errors in Tool-Use LLMs [77.79172008184415]
  SpecTool is a new benchmark to identify error patterns in LLM output on tool-use tasks. We show that even the most prominent LLMs exhibit these error patterns in their outputs. Researchers can use the analysis and insights from SPECTOOL to guide their error mitigation strategies.
  arXiv  Detail & Related papers  (2024-11-20T18:56:22Z)
• MdEval: Massively Multilingual Code Debugging [37.48700033342978]
  We propose the first massively multilingual debug benchmark, which includes 3.6K test samples of 18 programming languages. We introduce the instruction corpora MDEVAL-INSTRUCT by injecting bugs into the correct multilingual queries and solutions. Our experiments on MDEVAL reveal a notable performance gap between open-source models and closed-source LLMs.
  arXiv  Detail & Related papers  (2024-11-04T17:36:40Z)
• Test smells in LLM-Generated Unit Tests [11.517293765116307]
  This study explores the diffusion of test smells in Large Language Models generated unit test suites. We analyze a benchmark of 20,500 LLM-generated test suites produced by four models across five prompt engineering techniques. We identify and analyze the prevalence and co-occurrence of various test smells in both human written and LLM-generated test suites.
  arXiv  Detail & Related papers  (2024-10-14T15:35:44Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts. We find that Llama Instruct and Mistral models exhibit high degrees of language confusion. We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents [10.730852617039451]
  We investigate the capability of LLM-based Code Agents to formalize user issues into test cases.<n>We propose a novel benchmark based on popular GitHub repositories, containing real-world issues, ground-truth bug-fixes, and golden tests.<n>We find that LLMs generally perform surprisingly well at generating relevant test cases, with Code Agents designed for code repair exceeding the performance of systems designed for test generation.
  arXiv  Detail & Related papers  (2024-06-18T14:54:37Z)
• TESTEVAL: Benchmarking Large Language Models for Test Case Generation [15.343859279282848]
  We propose TESTEVAL, a novel benchmark for test case generation with large language models (LLMs)<n>We collect 210 Python programs from an online programming platform, LeetCode, and design three different tasks: overall coverage, targeted line/branch coverage, and targeted path coverage.<n>We find that generating test cases to cover specific program lines/branches/paths is still challenging for current LLMs.
  arXiv  Detail & Related papers  (2024-06-06T22:07:50Z)
• Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs [5.549095839198671]
  Large Language Models (LLMs) have shown remarkable capabilities in processing both natural and programming languages. We propose a novel method to assess the code understanding performance of LLMs, particularly focusing on subtle differences between code and its descriptions. We apply different types of code mutations, such as operator replacement and statement deletion, to generate inconsistent code-description pairs. We conduct a case study on the two popular LLMs, GPT-3.5 and GPT-4, using the state-of-the-art code generation benchmark, HumanEval-X.
  arXiv  Detail & Related papers  (2024-01-11T14:27:43Z)
• 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)
• Native Language Identification with Large Language Models [60.80452362519818]
  We show that GPT models are proficient at NLI classification, with GPT-4 setting a new performance record of 91.7% on the benchmark11 test set in a zero-shot setting. We also show that unlike previous fully-supervised settings, LLMs can perform NLI without being limited to a set of known classes.
  arXiv  Detail & Related papers  (2023-12-13T00:52:15Z)
• Prompting Code Interpreter to Write Better Unit Tests on Quixbugs Functions [0.05657375260432172]
  Unit testing is a commonly-used approach in software engineering to test the correctness and robustness of written code. In this study, we explore the effect of different prompts on the quality of unit tests generated by Code Interpreter. We find that the quality of the generated unit tests is not sensitive to changes in minor details in the prompts provided.
  arXiv  Detail & Related papers  (2023-09-30T20:36:23Z)
• Intergenerational Test Generation for Natural Language Processing Applications [16.63835131985415]
  We propose an automated test generation method for detecting erroneous behaviors of various NLP applications. We implement this method into NLPLego, which is designed to fully exploit the potential of seed sentences. NLPLego successfully detects 1,732, 5301, and 261,879 incorrect behaviors with around 95.7% precision in three tasks.
  arXiv  Detail & Related papers  (2023-02-21T07:57:59Z)
• Zero-Shot Cross-lingual Semantic Parsing [56.95036511882921]
  We study cross-lingual semantic parsing as a zero-shot problem without parallel data for 7 test languages. We propose a multi-task encoder-decoder model to transfer parsing knowledge to additional languages using only English-Logical form paired data. Our system frames zero-shot parsing as a latent-space alignment problem and finds that pre-trained models can be improved to generate logical forms with minimal cross-lingual transfer penalty.
  arXiv  Detail & Related papers  (2021-04-15T16:08:43Z)
• X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models [103.75890012041366]
  Language models (LMs) have proven surprisingly successful at capturing factual knowledge. However, studies on LMs' factual representation ability have almost invariably been performed on English. We create a benchmark of cloze-style probes for 23 typologically diverse languages.
  arXiv  Detail & Related papers  (2020-10-13T05:29:56Z)

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.