一句话：针对现有深度学习变异故障构造的句法正确性与有效性问题，探索学习构造更优变异故障的方法。
变异故障是变异测试以及许多软件测试与调试任务的核心，因此构造高质量变异故障十分关键。传统变异技术依赖有限的人工定义变异算子来生成句法变异故障。当前最先进的深度学习方法 DeepMutation 通过经典序列到序列神经机器翻译从真实故障中学习构造变异故障，但无法保证所构造变异故障的句法正确性，并且因将每个目标方法简单视为词元序列而受限于巨大搜索空间和有限特征，存在有效性不足问题。该文旨在学习构造更好的变异故障，但所给摘要未展开具体方法、实验设置与结果。
Mutation faults are the core of mutation testing and have been widely used in many other software testing and debugging tasks. Hence, constructing high-quality mutation faults is critical. There are many traditional mutation techniques that construct syntactic mutation faults based on a limited set of manually-defined mutation operators. To improve them, the state-of-the-art deep-learning (DL) based technique (i.e., DeepMutation) has been proposed to construct mutation faults by learning from real faults via classic sequence-to-sequence neural machine translation (NMT). However, its performance is not satisfactory since it cannot ensure syntactic correctness of constructed mutation faults and suffers from the effectiveness issue due to the huge search space and limited features by simply treating each targeted method as a token stream.