Analyzing the Predictability of Source Code and its Application in Creating Parallel Corpora for English-to-Code Statistical Machine Translation

Analyzing source code using computational linguistics and exploiting the linguistic properties of source code have recently become popular topics in the domain of software engineering. In the first part of the thesis, we study the predictability of source code and determine how well source code can be represented using language models developed for natural language processing. In the second part, we study how well English discussions of source code can be aligned with code elements to create parallel corpora for English-to-code statistical machine translation. This work is organized as a “manuscript” thesis whereby each core chapter constitutes a submitted paper. The first part replicates recent works that have concluded that software is more repetitive and predictable, i.e. more natural, than English texts. We find that much of the apparent “naturalness” is artificial and is the result of language specific tokens. For example, the syntax of a language, especially the separators e.g., semi-colons and brackets, make up for 59% of all uses of Java tokens in our corpus. Furthermore, 40% of all 2-grams end in a separator, implying that a model for autocompleting the next token, would have a trivial separator as top suggestion 40% of the time. By using the standard NLP practice of eliminating punctuation (e.g., separators) and stopwords (e.g., keywords) we find that code is less repetitive and predictable than was suggested by previous work. We replicate this result across 7 programming languages. Continuing this work, we find that unlike the code written for a particular project, API code usage is similar across projects. For example a file is opened and closed in the same manner irrespective of domain. When we restrict our n-grams to those contained in the Java API we find that the entropy for 2-grams is significantly lower than the English corpus. This repetition perhaps explains the successful literature on API usage suggestion and autocompletion. We then study the impact of the representation of code on repetition. The n-gram model assumes that the current token can be predicted by the sequence of n previous tokens. When we extract program graphs of size 2, 3, and 4 nodes we see that the abstract graph representation is much more concise and repetitive than the n-gram representations of the same code. This suggests that future work should focus on graphs that include control and data flow dependencies and not linear sequences of tokens. The second part of this thesis focuses cleaning English and code corpora to aid in machine translation. Generating source code API sequences from an English query using Machine Translation (MT) has gained much interest in recent years. For any kind of MT, the model needs to be trained on a parallel corpus. We clean StackOverflow, one of the most popular online discussion forums for programmers, to generate a parallel English-Code corpora. We contrast three data cleaning approaches: standard NLP, title only, and software task. We evaluate the quality of each corpus for MT. We measure the corpus size, percentage of unique tokens, and per-word maximum likelihood alignment entropy. While many works have shown that code is repetitive and predictable, we find that English discussions of code are also repetitive. Creating a maximum likelihood MT model, we find that English words map to a small number of specific code elements which partially explains the success of using StackOverflow for search and other tasks in the software engineering literature and paves the way for MT. Our scripts and corpora are publicly available