POSIT: Simultaneously tagging natural and programming languages

• Main Authors: PÂRȚACHI, Profir-Petru, DASH, Santanu, TREUDE, Christoph, BARR, Earl T.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Code-switching; Language identification; Mixed-code; Part-of-speech tagging; Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8907; https://ink.library.smu.edu.sg/context/sis_research/article/9910/viewcontent/icse20a.pdf
• Institution: Singapore Management University

Software developers use a mix of source code and natural language text to communicate with each other: Stack Overflow and Developer mailing lists abound with this mixed text. Tagging this mixed text is essential for making progress on two seminal software engineering problems — traceability, and reuse via precise extraction of code snippets from mixed text. In this paper, we borrow code-switching techniques from Natural Language Processing and adapt them to apply to mixed text to solve two problems: language identification and token tagging. Our technique, POSIT, simultaneously provides abstract syntax tree tags for source code tokens, part-of-speech tags for natural language words, and predicts the source language of a token in mixed text. To realize POSIT, we trained a biLSTM network with a Conditional Random Field output layer using abstract syntax tree tags from the CLANG compiler and part-of-speech tags from the Standard Stanford part-of-speech tagger. POSIT improves the state-of-the-art on language identification by 10.6% and PoS/AST tagging by 23.7% in accuracy