一句话：结合增量调试与数据溯源，为 Spark 程序自动定位复现测试失败的最小输入记录集。
大数据分析开发常因数据不干净或错误假设而依赖试错调试；程序崩溃或异常结果出现时，开发者希望定位根因。BigSift 以 Apache Spark 程序、用户定义的测试判定函数和数据集为输入，将增量调试（delta debugging）与数据溯源结合，输出能够复现同一测试失败的最小输入记录集。其技术贡献在于设计系统优化，使自动调试在数据密集型可扩展计算中更接近实用。摘要未报告具体实验与量化结果。
Developing Big Data Analytics often involves trial and error debugging, due to the unclean nature of datasets or wrong assumptions made about data. When errors (e.g. program crash, outlier results, etc.) arise, developers are often interested in pinpointing the root cause of errors. To address this problem, BigSift takes an Apache Spark program, a user-defined test oracle function, and a dataset as input and outputs a minimum set of input records that reproduces the same test failure by combining the insights from delta debugging with data provenance. The technical contribution of BigSift is the design of systems optimizations that bring automated debugging closer to a reality for data intensive scalable computing.