SEDGE: Symbolic example data generation for dataflow programs

Exhaustive, automatic testing of dataflow (esp. mapreduce) programs has emerged as an important challenge. Past work demonstrated effective ways to generate small example data sets that exercise operators in the Pig platform, used to generate Hadoop map-reduce programs. Although such prior technique...

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Published in:2013 IEEE/ACM 28th International Conference on Automated Software Engineering (ASE) pp. 235 - 245
Main Authors: Kaituo Li, Reichenbach, Christoph, Smaragdakis, Yannis, Diao, Yanlei, Csallner, Christoph
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2013
Subjects:
Benchmark testing
Cognition
Concrete
Data processing
Educational institutions
Extraterrestrial measurements
Programming
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Abstract Exhaustive, automatic testing of dataflow (esp. mapreduce) programs has emerged as an important challenge. Past work demonstrated effective ways to generate small example data sets that exercise operators in the Pig platform, used to generate Hadoop map-reduce programs. Although such prior techniques attempt to cover all cases of operator use, in practice they often fail. Our SEDGE system addresses these completeness problems: for every dataflow operator, we produce data aiming to cover all cases that arise in the dataflow program (e.g., both passing and failing a filter). SEDGE relies on transforming the program into symbolic constraints, and solving the constraints using a symbolic reasoning engine (a powerful SMT solver), while using input data as concrete aids in the solution process. The approach resembles dynamic-symbolic (a.k.a. "concolic") execution in a conventional programming language, adapted to the unique features of the dataflow domain. In third-party benchmarks, SEDGE achieves higher coverage than past techniques for 5 out of 20 PigMix benchmarks and 7 out of 11 SDSS benchmarks and (with equal coverage for the rest of the benchmarks). We also show that our targeting of the high-level dataflow language pays off: for complex programs, state-of-the-art dynamic-symbolic execution at the level of the generated map-reduce code (instead of the original dataflow program) requires many more test cases or achieves much lower coverage than our approach.
AbstractList Exhaustive, automatic testing of dataflow (esp. mapreduce) programs has emerged as an important challenge. Past work demonstrated effective ways to generate small example data sets that exercise operators in the Pig platform, used to generate Hadoop map-reduce programs. Although such prior techniques attempt to cover all cases of operator use, in practice they often fail. Our SEDGE system addresses these completeness problems: for every dataflow operator, we produce data aiming to cover all cases that arise in the dataflow program (e.g., both passing and failing a filter). SEDGE relies on transforming the program into symbolic constraints, and solving the constraints using a symbolic reasoning engine (a powerful SMT solver), while using input data as concrete aids in the solution process. The approach resembles dynamic-symbolic (a.k.a. "concolic") execution in a conventional programming language, adapted to the unique features of the dataflow domain. In third-party benchmarks, SEDGE achieves higher coverage than past techniques for 5 out of 20 PigMix benchmarks and 7 out of 11 SDSS benchmarks and (with equal coverage for the rest of the benchmarks). We also show that our targeting of the high-level dataflow language pays off: for complex programs, state-of-the-art dynamic-symbolic execution at the level of the generated map-reduce code (instead of the original dataflow program) requires many more test cases or achieves much lower coverage than our approach.
Author Kaituo Li
Reichenbach, Christoph
Diao, Yanlei
Csallner, Christoph
Smaragdakis, Yannis
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  surname: Reichenbach
  fullname: Reichenbach, Christoph
  organization: Inst. of Inf., Goethe Univ. Frankfurt, Frankfurt am Main, Germany
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  givenname: Yannis
  surname: Smaragdakis
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  organization: Dept. of Inf., Univ. of Athens, Athens, Greece
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  organization: Comput. Sci. Dept., Univ. of Massachusetts, Amherst, MA, USA
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  givenname: Christoph
  surname: Csallner
  fullname: Csallner, Christoph
  organization: Comput. Sci. & Eng., Univ. of Texas at Arlington, Arlington, TX, USA
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Snippet Exhaustive, automatic testing of dataflow (esp. mapreduce) programs has emerged as an important challenge. Past work demonstrated effective ways to generate...
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StartPage 235
SubjectTerms Benchmark testing
Cognition
Concrete
Data processing
Educational institutions
Extraterrestrial measurements
Programming
Title SEDGE: Symbolic example data generation for dataflow programs
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