一句话：提出结合程序切片、层次化 delta debugging 与 Kotlin 特有变换的缺陷缩减方法，并实现工具 ReduKtor。
编译器开发中的缺陷定位通常依赖 delta debugging，即把触发缺陷的输入程序缩减为最小复现用例，但每种新语言都有独特语法特性，使现有工具与方法的复用效率大打折扣。本文是一篇经验论文，针对 JetBrains 较新的语言 Kotlin，提出一种新的组合方法：程序切片、层次化 delta debugging 与 Kotlin 特有变换，三者相互协同。作者在原型工具 ReduKtor 中实现了该方法，并在合成与真实 Kotlin 程序上进行了大量评估，还与经典 delta debugging 技术做了性能对比。评估结果支持该方法在 Kotlin delta debugging 上的实际可用性，也表明要同时使用语言无关与语言相关技术，才能获得最佳的缩减效率与性能。
Bug localization is well-known to be a difficult problem in software engineering, and specifically in compiler development, where it is beneficial to reduce the input program to a minimal reproducing example; this technique is more commonly known as delta debugging. What additionally contributes to the problem is that every new programming language has its own unique quirks and foibles, making it near impossible to reuse existing tools and approaches with full efficiency. In this experience paper we tackle the delta debugging problem w.r.t. Kotlin, a relatively new programming language from JetBrains. Our approach is based on a novel combination of program slicing, hierarchical delta debugging and Kotlin-specific transformations, which are synergistic to each other. We implemented it in a prototype called ReduKtor and did extensive evaluation on both synthetic and real Kotlin programs; we also compared its performance with classic delta debugging techniques. The evaluation results support the practical usability of our approach to Kotlin delta debugging and also shows the importance of using both language-agnostic and language-specific techniques to achieve best reduction efficiency and performance.