Programmers often write code that has similarity to existing code written somewhere. A tool that could help programmers to search such similar code would be immensely useful. Such a tool could help programmers to extend partially written code snippets to completely implement necessary functionality, help to discover extensions to the partial code which are commonly included by other programmers, help to cross-check against similar code written by other programmers, or help to add extra code which would fix common mistakes and errors. We propose Aroma, a tool and technique for code recommendation via structural code search. Aroma indexes a huge code corpus including thousands of open-source projects, takes a partial code snippet as input, searches the corpus for method bodies containing the partial code snippet, and clusters and intersects the results of the search to recommend a small set of succinct code snippets which both contain the query snippet and appear as part of several methods in the corpus. We evaluated Aroma on 2000 randomly selected queries created from the corpus, as well as 64 queries derived from code snippets obtained from Stack Overflow, a popular website for discussing code. We implemented Aroma for 4 different languages, and developed an IDE plugin for Aroma. Furthermore, we conducted a study where we asked 12 programmers to complete programming tasks using Aroma, and collected their feedback. Our results indicate that Aroma is capable of retrieving and recommending relevant code snippets efficiently. few code snippets shown in Listings 2, 3, which demonstrate how to configure the decoder to use less memory, and how to handle potential runtime exceptions, respectively. We call this the code recommendation problem. InputStream input = manager.open(fileName); Bitmap image = BitmapFactory.decodeStream(input); Listing 1. Suppose an Android programmer writes this code to decode a bitmap. final BitmapFactory.Options options = new BitmapFactory.Options(); options.inSampleSize = 2; Bitmap bmp = BitmapFactory.decodeStream(is, null, options); Listing 2. A recommended code snippet that shows how to configure the decoder to use less memory. Recommended lines are highlighted. 1 try { InputStream is = am.open(fileName); image = BitmapFactory.decodeStream(is); is.close(); } catch (IOException e) { // ... } Listing 3. Another recommended code snippet that shows how to properly close the input stream and handle any potential IOException. Recommended lines are highlighted. 2

Modern software engineering often involves using many existing APIs, both open source and-in industrial coding environments-proprietary. Programmers reference documentation and code search tools to remind themselves of proper common usage patterns of APIs. However, high-quality API usage examples are computationally expensive to curate and maintain, and API usage examples retrieved from company-wide code search can be tedious to review. We present a tool, EG, that mines codebases and shows the common, idiomatic usage examples for API methods. EG was integrated into Facebook's internal code search tool for the Hack language and evaluated on open-source GitHub projects written in Python. EG was also compared against code search results and handwritten examples from a popular programming website called ProgramCreek. Compared with these two baselines, examples generated by EG are more succinct and representative with less extraneous statements. In addition, a survey with Facebook developers shows that EG examples are preferred in 97% of cases. CCS CONCEPTS • Software and its engineering → Software maintenance tools.

No abstract

First, I would like to thank my research advisor, Norman Danner, who has consistently challenged and motivated me during my time at Wesleyan. He gave me my first opportunity to participate in research, for which I am very grateful. Thanks to Jim Lipton, Dan Licata, Saray Shai, and Danny Krizanc, who, along with Norman Danner, have taught wonderful computer science courses which I have had the pleasure of taking. An additional thanks to my readers, Dan Licata and Saray Shai, for reading and evaluating my thesis.

No abstract