Querying sequential software engineering data

Chengnian Sun, Haidong Zhang, Jian-Guang Lou, Hongyu Zhang, Qiang Wang, Dongmei Zhang, Siau-Cheng Khoo

Abstract

We propose a pattern-based approach to effectively and efficiently analyzing sequential software engineering (SE) data. Different from other types of SE data, sequential SE data preserves unique temporal properties, which cannot be easily analyzed without much programming effort. In order to facilitate the analysis of sequential SE data, we design a sequential pattern query language (SPQL), which specifies the temporal properties based on regular expressions, and is enhanced with variables and statements to store and manipulate matching states. We also propose a query engine to effectively process the SPQL queries. We have applied our approach to analyze two types of SE data, namely bug report history and source code change history. We experiment with 181,213 Eclipse bug reports and 323,989 code revisions of Android. SPQL enables us to explore interesting temporal properties underneath these sequential data with a few lines of query code and low matching overhead. The analysis results can help better under- stand a software process and identify process violations.

BibTeX

@inproceedings{Sun-al:FSE14,
  author    = {Chengnian Sun and
               Haidong Zhang and
               Jian{-}Guang Lou and
               Hongyu Zhang and
               Qiang Wang and
               Dongmei Zhang and
               Siau{-}Cheng Khoo},
  title     = {Querying sequential software engineering data},
  booktitle = {FSE},
  pages     = {700--710},
  publisher = {{ACM}},
  year      = {2014},
}

Querying sequential software engineering data

Abstract

Related papers