NaNofuzz: A Usable Tool for Automatic Test Generation
Matthew C. Davis, Sangheon Choi, Sam Estep, Brad A. Myers, Joshua Sunshine
摘要
In the United States alone, software testing labor is estimated to cost $48 billion USD per year. Despite widespread test execution automation and automation in other areas of software engineering, test suites continue to be created manually by software engineers. We have built a test generation tool, called NaNofuzz, that helps users find bugs in their code by suggesting tests where the output is likely indicative of a bug, e.g., that return NaN (not-a-number) values. NaNofuzz is an interactive tool embedded in a development environment to fit into the programmer's workflow. NaNofuzz tests a function with as little as one button press, analyses the program to determine inputs it should evaluate, executes the program on those inputs, and categorizes outputs to prioritize likely bugs. We conducted a randomized controlled trial with 28 professional software engineers using NaNofuzz as the intervention treatment and the popular manual testing tool, Jest, as the control treatment. Participants using NaNofuzz on average identified bugs more accurately (p < .05, by 30%), were more confident in their tests (p < .03, by 20%), and finished their tasks more quickly (p < .007, by 30%).
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- Rug: Turbo Llm for Rust Unit Test GenerationXiang Cheng, Fan Sang, Yizhuo Zhai, Xiaokuan Zhang 等ICSE 2025 · 被引用 6 次
- TerzoN: Human-in-the-Loop Software Testing with a Composite OracleMatthew C. Davis, Amy Wei, Brad A. Myers, Joshua SunshineFSE 2025 · 被引用 2 次
- Mock Deep Testing: Toward Separate Development of Data and Models for Deep LearningRuchira Manke, Mohammad Wardat, Foutse Khomh, Hridesh RajanICSE 2025
它引用的顶会 Paper3
- UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating FuzzersYuwei Li, Shouling Ji, Yuan Chen, Sizhuang Liang 等USENIX Security 2021 · 被引用 142 次
- Using Lightweight Formal Methods to Validate a Key-Value Storage Node in Amazon S3James Bornholt, Rajeev Joshi, Vytautas Astrauskas, Brendan Cully 等SOSP 2021 · 被引用 63 次
- DeepTC-Enhancer: Improving the Readability of Automatically Generated TestsDevjeet Roy, Ziyi Zhang, Maggie Ma, Venera Arnaoudova 等ASE 2020 · 被引用 32 次
相关 Paper
- A Qualitative Analysis of Fuzzer Usability and ChallengesYunze Zhao, Wentao Guo, Harrison Goldstein, Daniel Votipka 等CCS 2025
- UTopia: Automatic Generation of Fuzz Driver using Unit TestsBokdeuk Jeong, Joonun Jang, Hayoon Yi, Jiin Moon 等S&P 2023
- A Usability Evaluation of AFL and libFuzzer with CS StudentsStephan Plöger, Mischa Meier, Matthew SmithCHI 2023 · 被引用 9 次
- Leveraging Large Language Models for Enhancing the Understandability of Generated Unit TestsAmirhossein Deljouyi, Roham Koohestani, Maliheh Izadi, Andy ZaidmanICSE 2025 · 被引用 8 次
- ProphetFuzz: Fully Automated Prediction and Fuzzing of High-Risk Option Combinations with Only Documentation via Large Language ModelDawei Wang, Geng Zhou, Li Chen, Dan Li 等CCS 2024 · 被引用 9 次
