Enhanced Multi-Verse Optimizer (TMVO) and Applying it in Test Data Generation for Path Testing

Mohammad Hashem Ryalat, Hussam N. Fakhouri, Jamal Zraqou, Faten Hamad, Mamon S. Alzboun, Ahmad K. Al hwaitat

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Data testing is a vital part of the software development process, and there are various approaches available to improve the exploration of all possible software code paths. This study introduces two contributions. Firstly, an improved version of the Multi-verse Optimizer called Testing Multi-Verse Optimizer (TMVO) is proposed, which takes into account the movement of the swarm and the mean of the two best solutions in the universe. The particles move towards the optimal solution by using a mean-based algorithm model, which guarantees efficient exploration and exploitation. Secondly, TMVO is applied to automatically develop test cases for structural data testing, particularly path testing. Instead of automating the entire testing process, the focus is on centralizing automated procedures for collecting testing data. Automation for generating testing data is becoming increasingly popular due to the high cost of manual data generation. To evaluate the effectiveness of TMVO, it was tested on various well-known functions as well as five programs that presented unique challenges in testing. The test results indicated that TMVO performed better than the original MVO algorithm on the majority of the tested functions
Original languageEnglish
Pages (from-to)662-673
Number of pages12
JournalInternational Journal of Advanced Computer Science and Applications
Issue number2
Publication statusPublished - Jan 1 2023


  • multi-verse optimizer
  • MVO
  • optimization
  • swarm intelligence
  • testing

ASJC Scopus subject areas

  • Computer Science(all)

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