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MOBS: multi-operator observation-based slicing using lexical approximation of program dependence

Published: 27 May 2018 Publication History

Abstract

Observation-Based Slicing (ORBS) is a recently introduced program slicing technique based on direct observation of program semantics. Previous ORBS implementations slice a program by iteratively deleting adjacent lines of code. This paper introduces two new deletion operators based on lexical similarity. Furthermore, it presents a generalization of O RBS that can exploit multiple deletion operators: Multi-operator Observation-Based Slicing (MOBS). An empirical evaluation of MOBS using three real world Java projects finds that the use of lexical information, improves the efficiency of ORBS: MOBS can delete up to 87% of lines while taking only about 33% of the execution time with respect to the original ORBS.

References

[1]
David Binkley, Nicolas Gold, M. Harman, Syed Islam, Jens Krinke, and Shin Yoo. 2014. ORBS: Language-Independent Program Slicing. In Proceedings of the 22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2014). 109--120.
[2]
David E. Goldberg. 1989. Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading, MA.

Cited By

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  • (2022)Language-agnostic dynamic analysis of multilingual code: promises, pitfalls, and prospectsProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3560880(1621-1626)Online publication date: 7-Nov-2022

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Published In

cover image ACM Conferences
ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings
May 2018
231 pages
ISBN:9781450356633
DOI:10.1145/3183440
  • Conference Chair:
  • Michel Chaudron,
  • General Chair:
  • Ivica Crnkovic,
  • Program Chairs:
  • Marsha Chechik,
  • Mark Harman
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

View all
  • (2022)Language-agnostic dynamic analysis of multilingual code: promises, pitfalls, and prospectsProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3560880(1621-1626)Online publication date: 7-Nov-2022

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