  Existing template and learning-based APR tools have successfully found
patches for many benchmark faults. However, our analysis of existing results
shows that omission faults pose a significant challenge to these techniques.
For template based approaches, omission faults provide no location to apply
templates to; for learning based approaches that formulate repair as Neural
Machine Translation (NMT), omission faults similarly do not provide the faulty
code to translate. To address these issues, we propose GLAD, a novel
learning-based repair technique that specifically targets if-clause synthesis.
GLAD does not require a faulty line as it is based on generative Language
Models (LMs) instead of machine translation; consequently, it can repair
omission faults. GLAD intelligently constrains the language model using a
type-based grammar. Further, it efficiently reduces the validation cost by
performing dynamic ranking of candidate patches using a debugger. Thanks to the
shift from translation to synthesis, GLAD is highly orthogonal to existing
techniques: GLAD can correctly fix 16 Defects4J v1.2 faults that previous
NMT-based techniques could not, while maintaining a reasonable runtime cost,
underscoring its utility as an APR tool and potential to complement existing
tools in practice. An inspection of the bugs that GLAD fixes reveals that GLAD
can quickly generate expressions that would be challenging for other
techniques.
