Cuckoo Search Algorithm for Test Case Prioritization in Regression Testing
Priyanka Dhareula1, Anita Ganpati2

1Priyanka Dhareula, Computer Science Department, H. P. University, Shimla – 171005, H. P., India.
2Anita Ganpati, Computer Science Department, H. P. University, Shimla – 171005, H. P., India

Manuscript received on 11 August 2019. | Revised Manuscript received on 20 August 2019. | Manuscript published on 30 September 2019. | PP: 6004-6009 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4488098319/2019©BEIESP | DOI: 10.35940/ijrte.C4488.098319
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Abstract: There are countless optimization problems that have been accelerated by Nature Inspired Metaheuristic Optimization Algorithms (NIMOA) in the earlier decades. NIMOA have gained huge popularity owing to their effective results. In this study NIMOA namely, Cuckoo Search Algorithm (CSA) is used to prioritize (order) the test cases for Regression Testing (RT). Prioritizations aids in the execution of higher priority test cases to give early fault detection. This research adopts the aggressive approach of reproduction made by Cuckoos to prioritize the test cases for RT. Average Percentage of Fault Detected (APFD) metrics is used in this paper for validations of results. APFD metrics is used to compare the performance of CSA with Flower Pollination Algorithm (FPA) and traditional approaches for Test Case Prioritization (TCP). Two java applications are used for the study. CSA is implemented in Java on eclipse platform. It is learnt from the study that APFD results of CSA outperformed the FPA for both the applications namely Puzzle Game and AreaandPerimeter. It is inferred from the results that prioritized set of test cases given by NIMOA outperformed the APFD results of traditional approaches and also CSA performed better than the FPA for TCP.
Key Terms: Cuckoo Search; Flower Pollination; Regression Testing; Test Case Prioritization; APFD

Scope of the Article:
Algorithmic Game Theory