Evolutionary algorithms for path coverage test data generation and optimization: a review

Being a time-consuming and costly activity, software testing always demands optimization and automation. Software testing is an important activity to achieve quality and customer satisfaction. This paper presents a comparative evaluation of different hybrid automated software testing techniques using the concepts of soft computing for overall quality enhancement. A comparison between three hybrid automation techniques is carried out i.e., hybrid ant colony optimization-genetic algorithms (ACO-GA), hybrid artificial bee colony (ABC)-Naïve Bayes, hybrid ABC-GA along with three parent approaches. The comparison is made by applying these hybrid techniques for the selection of minimized test suites thus reducing overall testing effort and eliminating useless or redundant test cases. The experimental results prove the efficiency of these hybrid approaches in different scenarios. The impact of automated testing techniques for quality enhancement is assessed in terms of defect density and defect detection percentage.

Section: Related Workmentioning

confidence: 99%

Hybrid swarm intelligence-based software testing techniques for improving quality of component based software

Palak

Gulia

Gill

2021

“…Mishra et al [16] introduced a systematic review of test data generation and optimization for addressing path testing by utilizing Evolutionary Algorithms (EAs). Several EA algorithms are used for automatic test case generation and optimization to achieve maximum path coverage.…”

Section: Related Workmentioning

confidence: 99%

Test suites effectiveness evolution in open source systems: empirical study

Akour¹,

Alenezi²

2020

Test suite code coverage is usually used to indicate the capability of a test suite in detecting faults. Earlier research studies, which explored the relationship among test suite effectiveness and code coverage, have not addressed this relationship evolutionally. Moreover, some of these works were studied small or identical domain systems, which make the result generalization process unclear for other systems. Finally, some of these studies were conducted with automatically generated test suites, which might not present the real situation for studied systems, so the results cannot be generalized to real test suites. In this paper, the authors empirically explore three open-source software systems along with their 11 versions. These versions are evolved over time and might have more sources of code and test suites. This work objective is to study the correlation between test suite effectiveness, the size of the test suite, and coverage for three Java programs during their evolution. In this work, the code coverage, test suite LOC and mutation testing coverage are measured to assess the correlation between the effectiveness of fault detection, code coverage, and test suite size. Based on the result we cannot generalize the assumption that test size is always revealing a positive correlation with its eﬀectiveness, but still weak to the high correlation between test effectiveness, test size, and coverage.

“…The particle swarm optimaiztion PSO algorithm is widely known for its ease of implementation and its ability to quickly converge to the optimal solution. The particle swarm optimaiztion PSO algorithm also has qualities and features that make it distinctly efficient as it does not require improvement of any scalable information of the function and uses simple logical factors, compared to other algorithms, we find it characterized by its speed, low cost and accuracy [22][23][24]. The pso algorithm is suitable for solving linear, non-linear, intermittent and continuous variables, so that is has been used as a robust method to solve optimization problems in a wide variety of applications [25][26][27], We work to reduce the error to a minimum in the numerical method used the new iterative method and thus arrive at the nearest approximate and analytical solution to the exact solution of the system Ito.…”

Section: Introductionmentioning

confidence: 99%

The best parameters selection using pso algorithm to solving for ito system by new iterative technique

Abed

Ahmad

2020

The main aim of this study is to obtain the best approximate solution for the nonlinear Ito system by applying the new iterative method, A new technique has been proposed that combines the new iterative method with the particle optimization algorithm. The most important distinctive of this work is the analysis of errors between the exact solution of the system and the approximate solutions, which showed us that these approximate solutions of the proposed technique in particular have high accuracy because they converge significantly from the exact solution.