Publication detail

Application of Evolutionary Algorithms for Optimization of Regression Suites

KEKELYOVÁ, M. ZACHARIÁŠOVÁ, M. KOTÁSEK, Z. HRUŠKA, T.

Original Title

Application of Evolutionary Algorithms for Optimization of Regression Suites

English Title

Application of Evolutionary Algorithms for Optimization of Regression Suites

Type

conference paper

Language

en

Original Abstract

Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not broken the intended functionality. However, because of the complexity of current hardware systems, it is desirable to have optimized regression suites that provide the highest verification coverage with minimal simulation time and resources. In this paper, we introduce a coverage-directed optimization algorithm for creating optimized regression suites from verification stimuli that were evaluated in simulation-based verification environment. The results of our experiments show that the quality and the size of the final regression suites are significantly improved in comparison to the original test suit. For our experimental system, we were able to eliminate 94.4% redundant stimuli from the original test suite while retaining the same level of functional coverage.

English abstract

Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not broken the intended functionality. However, because of the complexity of current hardware systems, it is desirable to have optimized regression suites that provide the highest verification coverage with minimal simulation time and resources. In this paper, we introduce a coverage-directed optimization algorithm for creating optimized regression suites from verification stimuli that were evaluated in simulation-based verification environment. The results of our experiments show that the quality and the size of the final regression suites are significantly improved in comparison to the original test suit. For our experimental system, we were able to eliminate 94.4% redundant stimuli from the original test suite while retaining the same level of functional coverage.

Keywords

genetic algorithm optimization regression tests

RIV year

2015

Released

22.04.2015

Publisher

IEEE Computer Society

Location

Belgrade

ISBN

978-1-4799-6779-7

Book

IEEE 18th International Symposium on Design and Diagnostics of Electronic Circuits and Systems

Edition

NEUVEDEN

Edition number

NEUVEDEN

Pages from

91

Pages to

94

Pages count

4

Documents

BibTex


@inproceedings{BUT119863,
  author="Michaela {Belešová} and Marcela {Zachariášová} and Zdeněk {Kotásek} and Tomáš {Hruška}",
  title="Application of Evolutionary Algorithms for Optimization of Regression Suites",
  annote="Regression test suites are necessary to ensure that
changes to the system made after bug fixes or reimplementation
have not broken the intended functionality. However, because of
the complexity of current hardware systems, it is desirable to have optimized
regression suites that provide the highest verification coverage with minimal
simulation time and resources. In this paper, we introduce a coverage-directed
optimization algorithm for creating optimized regression suites from verification
stimuli that were evaluated in simulation-based verification environment. The
results of our experiments show that the quality and the size of the final
regression suites are significantly improved in comparison to the original test
suit. For our experimental system, we were able to eliminate 94.4% redundant
stimuli from the original test suite while retaining the same level of functional
coverage.",
  address="IEEE Computer Society",
  booktitle="IEEE 18th International Symposium on Design and Diagnostics of Electronic Circuits and Systems",
  chapter="119863",
  doi="10.1109/DDECS.2015.42",
  edition="NEUVEDEN",
  howpublished="print",
  institution="IEEE Computer Society",
  year="2015",
  month="april",
  pages="91--94",
  publisher="IEEE Computer Society",
  type="conference paper"
}