What is it about?
The GLADE algorithm by Bastani et al., published at PLDI 2017, was the first black-box approach to claim context-free approximation of input specification for non-trivial languages. This paper replicates GLADE's experiments, to find out how effective GLADE is at inferring input grammars.
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Why is it important?
Prompted by recent observations that the GLADE algorithm may show lower performance than reported in the original paper, we have reimplemented the GLADE algorithm from scratch. Our evaluation confirms that the effectiveness score (F1) reported in the GLADE paper is overly optimistic, and in some cases, based on the wrong language. Furthermore, GLADE fares poorly in several real-world languages evaluated, producing grammars that spend megabytes to enumerate inputs.
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This page is a summary of: “Synthesizing input grammars”: a replication study, June 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3519939.3523716.
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