1. Regression Visualization and Annotation tool

3. Detailed method matching solution:

Given two methods m_1 and m_2, we calculate their signature and body similarity as follows:

• Signature similarity (return type, method name, and parameters):

  • Return type (sim_r): if m_1.return_type == m_2.return_type: sim_r = 1; otherwise sim_r = 0

  • Method name (sim_name):

    • We tokenize method name by camel convention or underscore, to have two token sequences nseq_1 and nseq_2, then computer the similarity by sim_name = 2*LCS(seq1, seq2)/(|seq_1| + |seq_2|)

  • Parameters (sim_param):

    • We can two sets of parameter types, i.e., Set_p1 and Set_p2, we calculate their Jaccard coefficient as the similarity, i.e., sim_param = Intersection(Set_p1 , Set_p2)/Union(Set_p1, Set_p2).

  • Finally, we have sim_sig = (sim_r + sim_name + sim_param)/3

• Body similarity:

  • We tokenize m_1 and m_2 into two token sequences seq_1 and seq_2, we compute the similarity by 2*LCS(seq1, seq2)/(|seq_1| + |seq_2|)

4. AST rewriting rules:

See details in this page.

5. Ground-truth regression dataset

Download the details in this link.

6. Collected regressions in open-world experiment

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7. Qualitative study for open-world experiment

See details in this page.