Boa: A language and infrastructure for analyzing ultra-large-scale software repositories

By Robert Dyer, Hoan Anh Nguyen, Hridesh Rajan, Tien Nhut Nguyen. 2013

417 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software, Program Analysis & Verification

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Citations in the corpus

Summarizing Source Code using a Neural Attention Model (2016, background): “Billions of lines of source code reside in online repositories (Dyer et al., 2013), and high quality code is often coupled with natural language (NL) in the form of instructions, comments, and documentation.”
Toward Deep Learning Software Repositories (2015, result): “However, we analyzed the diversity of the projects from the proposed metrics in Nagappan et al. [65] and compared our dataset to the projects available on Boa [66] and found 1,556 projects out of the 16,221 projects.”
Parameter-free probabilistic API mining across GitHub (2015, background): “An important recent piece of infrastructure for large-scale mining work on code is Boa [13].”
Curating GitHub for engineered software projects (2017, methodology): “We account for the correlation between repository size and the various dimensions proposed in our study by including repository size as a feature in both the score-based and Random Forest classifiers as suggested in Emam et al. (2001). 5.”
PyDriller: Python framework for mining software repositories (2018, background): “Boa [7]: Boa is a domain-specific language and infrastructure that eases MSR.”
RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information (2015, neutral): “Unfortunately, many commit messages are empty or contain insufficient information [39], [19].”
DéjàVu: a map of code duplicates on GitHub (2017, background): “[Dyer et al. 2013] have curated a large number of Java repositories and provide a domain specific language to help researchers mine data about software repositories.”
Automatically generating commit messages from diffs using neural machine translation (2017, methodology): “Unfortunately, programmers sometimes neglect commit messages [11], [36], likely due to the same time and market pressures that have been reported to affect many types of documentation [44], [12], [24].”
Learning API Usages from Bytecode: A Statistical Approach (2015, neutral): “Keywords Statistical model, API usage, mobile apps”