Multi-deterministic Prioritization of Regression Test Suite Compared: ACO and BCO

  • Shweta Singhal
  • Shivangi Gupta
  • Bharti Suri
  • Supriya Panda
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 452)

Abstract.

Regression Test Suite Prioritization has become a very prominent area of research in software engineering due to the advancements in the field of technology. Software development budget generally keeps very little room for the software maintenance phase. Hence instead of developing new test cases for any version of the software, it is intelligent to prioritize the available test suite to check the correctness of the available code. Researchers have come across many actual natural systems that are remarkable examples of solving any problem efficiently. In this paper we have compared the work of two nature inspired systems: Ant Colony Optimization (ACO), Bee Colony Optimization (BCO). The comparison has been analyzed using eight examples used to solve the regression test prioritization problem. The effectiveness of the two techniques discussed here have been compared using several metrics namely Average Efficiency (AE) and Average Percentage of Test Suite Size Reduction (ASR), Percent Average Execution Time Reduction (AETR).

Keywords.

Regression testing Ant colony optimization (ACO) Bee colony optimization (BCO) Average efficiency (AE) Average percentage of test suite size reduction (ASR) Percent average execution time reduction (AETR) 
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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Shweta Singhal
    • 1
  • Shivangi Gupta
    • 2
  • Bharti Suri
    • 1
  • Supriya Panda
    • 2
  1. 1.University School of Information and Communication TechnologyG.G.S. Indraprastha UniversityNew DelhiIndia
  2. 2.The Northcap UniversityGurgaonIndia

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