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Data structure repair has been proposed as an error recovery mechanism to increase software resilience when errors happen at runtime for a deployed system. Although substantial work has been done on data structure repair, scalability remains a key challenge and applicability remains rather restricted. We present two constraint-based data structure repair techniques that build on the well-known Korat solver, which introduced an effective backtracking search to find all object graphs that satisfy user given structural properties within the user given bounds. Our baseline technique repairs an erroneous data structure by adapting the Korat search. While this approach is effective for repairing a data structure, it suffers from re-exploration when applied on data structures with similar errors that recur due to a fault in software or hardware that is exercised repeatedly. We introduce memoization to amortize the cost of a Korat search over repeated repairs that overlap in search. Our experimental results show that our memoized technique is effective for repairing data structure errors that recur.

Enhancing constraint-based repair of data structure errors that recur using memoization