title: "Sloppy Python: Using Dynamic Analysis to Automatically Add Error Tolerance to Ad-Hoc Data Processing Scripts"
authors: Philip J. Guo
venue: International Workshop on Dynamic Analysis (WODA)
year: 2011
links:
- <a href="SlopPy-paper.htm">Blog post</a>

tweet: SlopPy lets existing Python data analysis scripts tolerate and recover from errors without crashing

abstract: >
  Programmers and data analysts get
  frustrated when their long-running data processing scripts crash without
  producing results, due to either bugs in their code or inconsistencies in
  data sources.  To alleviate this frustration, we developed a dynamic
  analysis technique that guarantees scripts will never crash: It converts all
  uncaught exceptions into special NA (Not Available) objects and continues
  executing rather than crashing.  Thus, imperfect scripts will run to
  completion and produce partial results and an error log, which is more
  informative than simply crashing with no results.  We implemented our
  technique as a "Sloppy" Python interpreter that automatically adds error
  tolerance to existing scripts without any programmer effort or run-time
  slowdown.

bibtex: >
  @inproceedings{GuoSlopPy2011,
   author = {Guo, Philip J.},
   title = {{Sloppy Python}: Using Dynamic Analysis to Automatically Add Error Tolerance to Ad-hoc Data Processing Scripts},
   booktitle = {Proceedings of the Ninth International Workshop on Dynamic Analysis},
   series = {WODA '11},
   year = {2011},
   isbn = {978-1-4503-0811-3},
   location = {Toronto, Ontario, Canada},
   pages = {35--40},
   numpages = {6},
   url = {http://doi.acm.org/10.1145/2002951.2002961},
   doi = {10.1145/2002951.2002961},
   acmid = {2002961},
   publisher = {ACM},
   address = {New York, NY, USA},
   keywords = {data processing, fault tolerance, scripting},
  }