J2Graph

Given a Java program (source code), J2Graph creates a graph representation of the source code.

J2Graph's graph has the following nodes:

• Non-terminal nodes: All non-terminal nodes that JDT's internal representation contains.
• Tokens: Tokens in the source code. The list of tokens is not complete, as it is basically reconstructed from the AST. The main tokens appear, yet, it does not contain parenthesis, colons, etc.
• Vocabulary: Words that are extracted from the source code. Words are derived after the tokens, e.g., someVariable becomes two words, "some" and "variable".
• Symbols: The symbols of the program. Variables and method names are examples.

J2Graph's graph has the following edges, inspired by Miltos et al [1] and Cvitkovic et al [2]:

Edge	Explanation
NEXT_TOKEN	two consecutive token nodes
CHILD	non-terminal nodes to their children nodes and tokens
NEXT_LEXICAL_USE	each token that is bound to a variable to its next lexical use.
ASSIGNED_FROM	the right hand side of an assignment expression to its left hand-side.
RETURNS_TO	all return statements to the method declaration node where control returns.
OCCURRENCE_OF	all token and syntax nodes that bind to a symbol to the respective symbol node.
SUBTOKEN_OF	each identifier token node to the vocabulary nodes of its subtokens.

J2Graph also generates a dot output, so that you can visualize the outcome.

Example

For the following source code:

class D {

    public void m1() {
        int total = 0;
        for(int i = 0; i < 10; i++) {
            total += i;
        }
        return total;
    }

}

J2Graph generates the following graph:

References

• [1] Allamanis, Miltiadis, Earl T. Barr, Soline Ducousso, and Zheng Gao. "Typilus: neural type hints." arXiv preprint arXiv:2004.10657 (2020).

• [2] Cvitkovic, Milan, Badal Singh, and Animashree Anandkumar. "Open vocabulary learning on source code with a graph-structured cache." In International Conference on Machine Learning, pp. 1475-1485. 2019. Harvard

License

Apache 2.0