CrystalBLEU: Precisely and Efficiently Measuring the Similarity of Code
Aryaz Eghbali, Michael Pradel. ASE 2022
Recent years have brought a surge of work on predicting pieces of source code, e.g., for code completion, code migration, program repair, or translating natural language into code. All this work faces the challenge of evaluating the quality of a prediction w.r.t. some oracle, typically in the form of a reference solution. A common evaluation metric is the BLEU score, an n-gram-based metric originally proposed for evaluating natural language translation, but adopted in software engineering because it can be easily computed on any programming language and enables automated evaluation at scale. However, a key difference between natural and programming languages is that in the latter, completely unrelated pieces of code may have many common n-grams simply because of the syntactic verbosity and coding conventions of programming languages. We observe that these trivially shared n-grams hamper the ability of the metric to distinguish between truly similar code examples and code examples that are merely written in the same language. This paper presents CrystalBLEU, an evaluation metric based on BLEU, that allows for precisely and efficiently measuring the similarity of code. Our metric preserves the desirable properties of BLEU, such as being language-agnostic, able to handle incomplete or partially incorrect code, and efficient, while reducing the noise caused by trivially shared n-grams. We evaluate CrystalBLEU on two datasets from prior work and on a new, labeled dataset of semantically equivalent programs. Our results show that CrystalBLEU can distinguish similar from dissimilar code examples 1.9–4.5 times more effectively, when compared to the original BLEU score and a previously proposed variant of BLEU for code.
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