Test Case Reduction Using Ant Colony Optimization for Object Oriented Program

Being a crucial step and deciding factor for software reliability, software testing has evolved a long way and always attracted researchers due to various inherent challenges. The quality of a software application depends on the effectiveness of the testing carried out during development and maintenance phase. Testing is a crucial but time consuming activity that influences the overall cost of software development. Thus a minimal but efficient test suite selection is the need of the hour. This paper presents a hybrid technique based on swarm based search technique and GA (Genetic Algorithm) for selection of promising test cases to reduce the overall development cost and time of the application. We took component based software into consideration as they offer some inherent advantages over traditional software development paradigms.

Section: Related Workmentioning

confidence: 99%

Hybrid swarm and GA based approach for software test case selection

Palak¹,

Gulia²

2019

“…All this suggest that the proposed coevolution method is able to balance the growth in both of the mutant and test case evolution. The potential issue in the proposed method is redundancy, this happens because we instill no redundancy mechanism in the method, but since redundancy is a common issue in testing [34], we later investigate the result the result for redundancy. After further analysis of the test case and mutant solution, we got the redundancy level as shown in Table 6.…”

Section: Coevolution Using Laboratory and Real World Casesmentioning

confidence: 99%

Coevolution of Second-order-mutant

Tuloli

Sitohang

Hendradjaya

2018

One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneously using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multi-problem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.

“…Swarm intelligence takes part in solving test problems. Some study was used Ant Colony Optimization to reduce test case [5]. Others previous study provided recommendations independent paths automatically by using Genetic Algorithms [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Determining Basis Test Paths Using Genetic Algorithm and J4

Arwan

Rusdianto

2018

Basis test paths is a method that uses a graph contains nodes as a representation of codes and the lines as a sequence of code execution steps. Determination of basis test paths can be generated using a Genetic Algorithm, but the drawback was the number of iterations affect the possibility of visibility of the appropriate basis path. When the iteration is less, there is a possibility the paths do not appear all. Conversely, if the iteration is too much, all the paths have appeared in the middle of iteration. This research aims to optimize the performance of Genetic Algorithms for the generation of Basis Test Paths by determining how many iterations level corresponding to the characteristics of the code. Code metrics Node, Edge, VG, NBD, LOC were used as features to determine the number of iterations. J48 classifier was employed as a method to predict the number of iterations. There were 17 methods have selected as a data training, and 16 methods as a data test. The system was able to predict 84.5% of 58 basis paths. Efficiency test results also show that our system was able to seek Basis Paths 35% faster than the old system.