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Existing template and learning-based APR tools have successfully foundpatches for many benchmark faults. However, our analysis of existing resultsshows that omission faults pose a significant challenge to these techniques.For template based approaches, omission faults provide no location to applytemplates to; for learning based approaches that formulate repair as NeuralMachine Translation (NMT), omission faults similarly do not provide the faultycode to translate. To address these issues, we propose GLAD, a novellearning-based repair technique that specifically targets if-clause synthesis.GLAD does not require a faulty line as it is based on generative LanguageModels (LMs) instead of machine translation; consequently, it can repairomission faults. GLAD intelligently constrains the language model using atype-based grammar. Further, it efficiently reduces the validation cost byperforming dynamic ranking of candidate patches using a debugger. Thanks to theshift from translation to synthesis, GLAD is highly orthogonal to existingtechniques: GLAD can correctly fix 16 Defects4J v1.2 faults that previousNMT-based techniques could not, while maintaining a reasonable runtime cost,underscoring its utility as an APR tool and potential to complement existingtools in practice. An inspection of the bugs that GLAD fixes reveals that GLADcan quickly generate expressions that would be challenging for othertechniques.

GLAD: Neural Predicate Synthesis to Repair Omission Faults