BigSift: automated debugging of big data analytics in data-intensive scalable computing

Muhammad Ali Gulzar, Siman Wang, Miryung Kim

Abstract

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.

BibTeX

@inproceedings{Gulzar-al:FSE18,
  author    = {Muhammad Ali Gulzar and
               Siman Wang and
               Miryung Kim},
  title     = {{BigSift:} automated debugging of big data analytics in data-intensive scalable computing},
  booktitle = {{ESEC/SIGSOFT} {FSE}},
  pages     = {863--866},
  publisher = {{ACM}},
  year      = {2018},
}

BigSift: automated debugging of big data analytics in data-intensive scalable computing

Abstract

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