Applying cluster on code to find inconsistency.

Key insight: Our approach is inspired by the observation that many bugs in software manifest as inconsistencies deviating from their non-buggy counterparts, namely the code snippets that implement the similar logic in the same codebase. Such bugs, regardless of their types, can be detected by identifying functionally-similar yet inconsistent code snippets in the same codebase

Pros

• Two-level clustering
• Embedding on program dependency graph
• Found bugs (at function level) in large projects
• Embedding on code structures
• Generality

Cons

• Need repo-specific training
• Literals are removed at the IR level

To abstract Constructs, we preserve only the variable types for each program statement and remove all variable names and versions.

• Needs repository-specific configuration of thresholds
• Still very high FP

Others

• Granularity at the function level

If an inconsistent cluster contains more than a fixed number (e.g., 2) of deviating nodes (i.e., nodes in Table 2), the inconsistency is deprioritized because it is unlikely to be a true inconsistency (i.e., a single inconsistency rarely involves many deviations).