GenSlice: Generalized Semantic History Slicing

Chenguang Zhu, Yi Li, Julia Rubin, and Marsha Chechik

In Proceedings of the 36th IEEE International Conference on Software Maintenance and Evolution (ICSME), 2020

Abstract: Semantic history slicing addresses the problem of identifying changes related to a particular high-level functionality from the software change histories. Existing solutions are either imprecise, resulting in larger-than-necessary history slices, or inefficient, taking a long time to execute. In this paper, we develop a generalized history slicing framework, named GenSlice, which overcomes the aforementioned limitations. GenSlice abstracts existing history slicing techniques and change history management operations (such as splitting commits into fine-grained changes) as history transformation operators, making it possible to apply them sequentially in various orders. We study and formally prove properties of various orders of operators and devise a systematic approach for efficiently producing history slices that are optimal for practical purposes. We report on an empirical evaluation of our framework, demonstrating its effectiveness on a set of real-world case studies.

Cite:

@inproceedings{Zhu2020GGS,
  author = {Zhu, Chenguang and Li, Yi and Rubin, Julia and Chechik, Marsha},
  booktitle = {Proceedings of the 36th IEEE International Conference on Software Maintenance and Evolution (ICSME)},
  month = sep,
  pages = {81--91},
  title = {{GenSlice}: Generalized Semantic History Slicing},
  year = {2020}
}