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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Bibliographic Details
Published in:Proceedings of ACM on programming languages Vol. 7; no. OOPSLA2; pp. 745 - 772
Main Authors: Pacak, André, Erdweg, Sebastian
Format: Journal Article
Language:English
Published: New York, NY, USA ACM 16.10.2023
Subjects:
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
Online Access:Get full text
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Summary: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