Divide-and-Conquer Approach for Multi-phase Statistical Migration for Source Code (T)

Abstract

Prior research shows that directly applying phrase-based SMT on lexical tokens to migrate Java to C# produces much semantically incorrect code. A key limitation is the use of sequences in phrase-based SMT to model and translate source code with well-formed structures. We propose mppSMT, a divide-and-conquer technique to address that with novel training and migration algorithms using phrase-based SMT in three phases. First, mppSMT treats a program as a sequence of syntactic units and maps/translates such sequences in two languages to one another. Second, with a syntax-directed fashion, it deals with the tokens within syntactic units by encoding them with semantic symbols to represent their data and token types. This encoding via semantic symbols helps better migration of API usages. Third, the lexical tokens corresponding to each sememe are mapped or migrated. The resulting sequences of tokens are merged together to form the final migrated code. Such divide-and-conquer and syntax-direction strategies enable phrase-based SMT to adapt well to syntactical structures in source code, thus, improving migration accuracy. Our empirical evaluation on several real-world systems shows that 84.8 -- 97.9% and 70 -- 83% of the migrated methods are syntactically and semantically correct, respectively. 26.3 -- 51.2% of total migrated methods are exactly matched to the human-written C# code in the oracle. Compared to Java2CSharp, a rule-based migration tool, it achieves higher semantic accuracy from 6.6 -- 57.7% relatively. Importantly, it does not require manual labeling for training data or manual definition of rules.