On Accelerating Ultra-Large-Scale Mining

By: Ganesha Upadhyaya and Hridesh Rajan

Abstract

Ultra-large-scale mining has been shown to be useful for a number of software engineering tasks e.g. mining specifications, defect prediction. We propose a new research direction for accelerating ultra-large-scale mining that goes beyond parallelization. Our key idea is to analyze the interaction pattern between the mining task and the artifact to cluster artifacts such that running the mining task on one candidate artifact from each cluster is sufficient to produce results for other artifacts in the same cluster. Our artifact clustering criteria go beyond syntactic, semantic, and functional similarities to mining-task-specific similarity, where the interaction pattern between the mining task and the artifact is used for clustering. Our preliminary evaluation demonstrates that our technique significantly reduces the overall mining time.

ACM Reference

Upadhyaya, G. and Rajan, H. 2017. On Accelerating Ultra-Large-Scale Mining. 39th IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track, ICSE-NIER, Buenos Aires, Argentina (2017), 39–42.

BibTeX Reference

@inproceedings{UpadhyayaRajan2017,
  author = {Ganesha Upadhyaya and Hridesh Rajan},
  title = {On Accelerating Ultra-Large-Scale Mining},
  booktitle = {39th IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track, ICSE-NIER, Buenos Aires, Argentina},
  pages = {39--42},
  year = {2017},
  publisher = {{IEEE} Computer Society},
  doi = {10.1109/ICSE-NIER.2017.11},
  abstract = {
  Ultra-large-scale mining has been shown to be useful for a number of
  software engineering tasks e.g. mining specifications, defect prediction.
  We propose a new research direction for accelerating ultra-large-scale
  mining that goes beyond parallelization. Our key idea is to analyze the
  interaction pattern between the mining task and the artifact to cluster
  artifacts such that running the mining task on one candidate artifact from
  each cluster is sufficient to produce results for other artifacts in the
  same cluster. Our artifact clustering criteria go beyond syntactic, semantic,
  and functional similarities to mining-task-specific similarity, where the
  interaction pattern between the mining task and the artifact is used for
  clustering. Our preliminary evaluation demonstrates that our technique
  significantly reduces the overall mining time.},
}