Boa: Ultra-Large-Scale Software Repository and Source-Code Mining

By: Robert Dyer, Hoan Anh Nguyen, Hridesh Rajan, and Tien N. Nguyen

Abstract

In today’s software-centric world, ultra-large-scale software repositories, such as SourceForge, GitHub, and Google Code, are the new library of Alexandria. They contain an enormous corpus of software and related information. Scientists and engineers alike are interested in analyzing this wealth of information. However, systematic extraction and analysis of relevant data from these repositories for testing hypotheses is hard, and best left for mining software repository (MSR) experts! Specifically, mining source code yields significant insights into software development artifacts and processes. Unfortunately, mining source code at a large scale remains a difficult task. Previous approaches had to either limit the scope of the projects studied, limit the scope of the mining task to be more coarse grained, or sacrifice studying the history of the code. In this article we address mining source code: (a) at a very large scale; (b) at a fine-grained level of detail; and (c) with full history information. To address these challenges, we present domain-specific language features for source-code mining in our language and infrastructure called Boa. The goal of Boa is to ease testing MSR-related hypotheses. Our evaluation demonstrates that Boa substantially reduces programming efforts, thus lowering the barrier to entry. We also show drastic improvements in scalability.

ACM Reference

Dyer, R. et al. 2015. Boa: Ultra-Large-Scale Software Repository and Source-Code Mining. ACM Trans. Softw. Eng. Methodol. 25, 1 (2015), 7:1–7:34. https://doi.org/10.1145/2803171.

BibTeX Reference

@article{DyerNguyenRajanNguyen2015b,
  author = {Robert Dyer and Hoan Anh Nguyen and Hridesh Rajan and Tien N. Nguyen},
  title = {Boa: Ultra-Large-Scale Software Repository and Source-Code Mining},
  journal = {{ACM} Trans. Softw. Eng. Methodol.},
  volume = {25},
  number = {1},
  pages = {7:1--7:34},
  year = {2015},
  doi = {10.1145/2803171},
  abstract = {
  In today's software-centric world, ultra-large-scale software repositories,
  such as SourceForge, GitHub, and Google Code, are the new library of
  Alexandria. They contain an enormous corpus of software and related
  information. Scientists and engineers alike are interested in analyzing this
  wealth of information. However, systematic extraction and analysis of relevant
  data from these repositories for testing hypotheses is hard, and best left for
  mining software repository (MSR) experts! Specifically, mining source code
  yields significant insights into software development artifacts and processes.
  Unfortunately, mining source code at a large scale remains a difficult task.
  Previous approaches had to either limit the scope of the projects studied,
  limit the scope of the mining task to be more coarse grained, or sacrifice
  studying the history of the code. In this article we address mining source
  code: (a) at a very large scale; (b) at a fine-grained level of detail; and
  (c) with full history information. To address these challenges, we present
  domain-specific language features for source-code mining in our language and
  infrastructure called Boa. The goal of Boa is to ease testing MSR-related
  hypotheses. Our evaluation demonstrates that Boa substantially reduces
  programming efforts, thus lowering the barrier to entry. We also show drastic
  improvements in scalability.},
}