journal article·2022Open access

Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

Manar Ahmed Hamza, Abdelzahir Abdelmaboud, Souad Larabi-Marie-Sainte, Haya Mesfer Alshahrani, Mesfer Al Duhayyim, Hamza Awad Ibrahim, Mohammed Rizwanullah, Ishfaq Yaseen

Computers, Materials & Continua· Volume 72· Issue 1

Abstract

Generally, software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software. But, the quality of test cases has a considerable influence on fault revealing capability of software testing activity. Test Case Prioritization (TCP) remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected (APFD) and time spent upon execution results. TCP is mainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics. The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing. In this aspect, the current study designs a Modified Harris Hawks Optimization based TCP (MHHO-TCP) technique for software testing. The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time. In addition, MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm. In order to validate the enhanced efficiency of MHHO-TCP technique, a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects. The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Software Testing and Debugging Techniques · Software System Performance and Reliability · Software Reliability and Analysis Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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