Interactive Debugging of Datalog Programs

Datalog is used for complex programming tasks nowadays, consisting of numerous inter-dependent predicates. But Datalog lacks interactive debugging techniques that support the stepwise execution and inspection of the execution state. In this paper, we propose interactive debugging of Datalog programs...

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Vydané v:Proceedings of ACM on programming languages Ročník 7; číslo OOPSLA2; s. 745 - 772
Hlavní autori: Pacak, André, Erdweg, Sebastian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 16.10.2023
Predmet:
Constraint and logic programming
Logic and databases
Operational semantics
Software and its engineering
Software testing and debugging
Theory of computation
debugging
Datalog
QSQR
small-step operational semantics
ISSN:2475-1421, 2475-1421
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Popis
Shrnutí:Datalog is used for complex programming tasks nowadays, consisting of numerous inter-dependent predicates. But Datalog lacks interactive debugging techniques that support the stepwise execution and inspection of the execution state. In this paper, we propose interactive debugging of Datalog programs following a top-down evaluation strategy called recursive query/subquery. While the recursive query/subquery approach is well-known in the literature, we are the first to provide a complete programming-language semantics based on it. Specifically, we develop the first small-step operational semantics for top-down Datalog, where subqueries occur as nested intermediate terms. The small-step semantics forms the basis of step-into interactions in the debugger. Moreover, we show how step-over interactions can be realized efficiently based on a hybrid Datalog semantics that adds a bottom-up database to our top-down operational semantics. We implemented a debugger for core Datalog following these semantics and explain how to adopt it for debugging the frontend languages of Soufflé and IncA. Our evaluation shows that our hybrid Datalog semantics can be used to debug real-world Datalog programs with realistic workloads.
ISSN:2475-1421
2475-1421
DOI:10.1145/3622824