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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.
Editorial
(2013)
The aim of this two-part special issue of The International Journal of Literary Linguistics is to probe the implications of the cognitive turn in literary linguistics that has gone hand in hand with the field’s growing appreciation of pragmatics at the end of the twentieth century (as illustrated e.g. by MacMahon, Mey, Verdonk and Weber). The view, increasingly shared by literary linguists, that literature is a communicative endeavour between text (author) and reader has resulted in a heightened interest in the cognitive abilities that ultimately make this communication possible. Yet the ease with which the buzzword ‘cognition’ sometimes is applied to explain these abilities and processes (which are often only assumed to exist rather than substantiated with empirical evidence) seems to be at odds not least with the fact that the cognitive sciences are far from a uniform field yielding fixed and finite results. Indeed, whether or not all language phenomena are underpinned by innate cognitive rules is an issue that remains a bone of contention amongst scholars, as does the nature of these cognitive rules themselves. Even a cognitive grammarian like Ronald Langacker warns against jumping to premature conclusions about the natural foundations of language (14) and avoids making such claims in his own work. ...