论文标题
深抹子：神经突变工具
DeepMutation: A Neural Mutation Tool
论文作者
论文摘要
突变测试可用于评估给定测试套件的断层检测能力。为此，突变测试框架的两个特征至关重要：（i）它们应产生代表实际断层的突变体； （ii）他们应该提供一个能够自动生成，注入和测试突变体的完整工具链。为了解决第一点，我们最近提出了一种使用经常性神经网络编码器架构体系结构的方法，以从〜787K的故障中学习突变体，从实际程序中挖出。对这种方法的经验评估证实了其产生代表实际断层的突变体的能力。在本文中，我们解决了第二点，提出了深刻的变化，该工具将我们的深度学习模型包裹到一个完全自动化的工具链中，能够生成，注入和测试突变体从实际故障中学到的突变体。视频：https：//sites.google.com/view/learning-moint/deepmunt
Mutation testing can be used to assess the fault-detection capabilities of a given test suite. To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject, and test the mutants. To address the first point, we recently proposed an approach using a Recurrent Neural Network Encoder-Decoder architecture to learn mutants from ~787k faults mined from real programs. The empirical evaluation of this approach confirmed its ability to generate mutants representative of real faults. In this paper, we address the second point, presenting DeepMutation, a tool wrapping our deep learning model into a fully automated tool chain able to generate, inject, and test mutants learned from real faults. Video: https://sites.google.com/view/learning-mutation/deepmutation
