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The development of correct and efficient software can be hindered bycompilation errors, which must be fixed to ensure the code's syntacticcorrectness and program language constraints. Neural network-based approacheshave been used to tackle this problem, but they lack guarantees of outputcorrectness and can require an unlimited number of modifications. Fixingcompilation errors within a given number of modifications is a challengingtask. We demonstrate that finding the minimum number of modifications to fix acompilation error is NP-hard. To address compilation error fixing problem, wepropose OrdinalFix, a complete algorithm based on shortest-path CFL(context-free language) reachability with attribute checking that is guaranteedto output a program with the minimum number of modifications required.Specifically, OrdinalFix searches possible fixes from the smallest to thelargest number of modifications. By incorporating merged attribute checking toenhance efficiency, the time complexity of OrdinalFix is acceptable forapplication. We evaluate OrdinalFix on two datasets and demonstrate its abilityto fix compilation errors within reasonable time limit. Comparing with existingapproaches, OrdinalFix achieves a success rate of 83.5%, surpassing allexisting approaches (71.7%).

OrdinalFix: Fixing Compilation Errors via Shortest-Path CFL Reachability