Context Software quality can be assured by going through software testing process. However, software testing phase is an expensive process as it consumes a longer time. By scheduling test cases execution order through a prioritization approach, software testing efficiency can be improved especially during regression testing. Objective It is a notable step to be taken in constructing important software testing environment so that a system's commercial value can increase. The main idea of this review is to examine and classify the current test case prioritization approaches based on the articulated research questions. Method Set of search keywords with appropriate repositories were utilized to extract most important studies that fulfill all the criteria defined and classified under journal, conference paper, symposiums and workshops categories. 69 primary studies were nominated from the review strategy. Results There were 40 journal articles, 21 conference papers, three workshop articles, and five symposium articles collected from the primary studies. As for the result, it can be said that TCP approaches are still broadly open for improvements. Each approach in TCP has specified potential values, advantages, and limitation. Additionally, we found that variations in the starting point of TCP process among the approaches provide a different timeline and benefit to project manager to choose which approaches suite with the project schedule and available resources. Conclusion Test case prioritization has already been considerably discussed in the software testing domain. However, it is commonly learned that there are quite a number of existing prioritization techniques that can still be improved especially in data used and execution process for each approach.

Test case prioritization approaches in regression testing: A systematic literature review

The goal of regression testing is to validate the modified software. Due to the resource and time constraints, it becomes necessary to develop techniques to minimize existing test suites by eliminating redundant test cases and prioritizing them. This paper proposes a 3-phase approach to solve test case prioritization. In the first phase, we are removing redundant test cases by simple matrix operation. In the second phase, test cases are selected from the test suite such that selected test cases represent the minimal set which covers all faults and also at the minimum execution time. For this phase, we are using multi objective particle swarm optimization (MOPSO) which optimizes fault coverage and execution time. In the third phase, we allocate priority to test cases obtained from the second phase. Priority is obtained by calculating the ratio of fault coverage to the execution time of test cases, higher the value of the ratio higher will be the priority and the test cases which are not selected in phase 2 are added to the test suite in sequential order. We have also performed experimental analysis based on maximum fault coverage and minimum execution time. The proposed MOPSO approach is compared with other prioritization techniques such as No Ordering, Reverse Ordering and Random Ordering by calculating Average Percentage of fault detected (APFD) for each technique and it can be concluded that the proposed approach outperformed all techniques mentioned above.

Test case prioritization using multi objective particle swarm optimizer

Regression testing is an expensive process. A number of methodologies of regression testing are used to improve its effectiveness. These are retest all, test case selection, test case reduction and test case prioritization. Retest all technique involves re-execution of all available test suites, which are critical moreover cost effective. In order to increase efficiency, test case prioritization is being utilized for rearranging the test cases. A number of algorithms has been stated in the literature survey such as Greedy Algorithms and Metaheuristic search algorithms. A simple greedy algorithm focuses on test case prioritization but results in less efficient manner, due to which researches moved towards the additional greedy and 2-Optimal algorithms. Forthcoming metaheuristic search technique (Hill climbing and Genetic Algorithm) produces a much better solution to the test case prioritization problem. It implements stochastic optimization while dealing with problem concern. The genetic algorithm is an evolutionary algorithm which gives an exact mathematical fitness value for the test cases on which prioritization is done. This paper focuses on the comparison of metaheuristic genetic algorithm with other algorithms and proves the efficiency of genetic algorithm over the remaining ones.

Test case prioritization techniques “an empirical study”

Tremendous systems are rapidly evolving based on the trendy Internet of Things (IoT) in various domains. Different technologies are used for communication between the massive connected devices through all layers of the IoT system, causing many security and performance issues. Regression and integration testing are considered repeatedly, in which the vast costs and efforts associated with the frequent execution of these inflated test suites hinder the adequate testing of such systems. This necessitates the focus on exploring innovative scalable testing approaches for large test suites in IoT-based systems. In this paper, a scalable framework for continuous integration and regression testing in IoT-based systems (IoT-CIRTF) is proposed, based on IoT-related criteria for test case prioritization and selection. The framework utilizes search-based techniques to provide an optimized prioritized set of test cases to select from. The selection is based on a trained prediction model for IoT standard components using supervised deep learning algorithms to continuously ensure the overall reliability of IoT-based systems. The experiments are held on two GSM datasets. The experimental results achieved prioritization accuracy up to 90% and 92% for regression testing and integration testing respectively. This provides an enhanced and efficient framework for continuous testing of IoT-based systems, as per IoT-related criteria for the prioritization and selection purposes.

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A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques

testing is the major process in software development life cycle. Regression testing is very costly and inevitable activity that is to be performed in a restricted environment to ensure the validity of modified software. It is inefficient to re- run every test case from test suite when some kind of modification is done in the software. Test case selection and prioritization techniques select and organize the test cases in a test suite based on some criteria such that the faults are covered quickly with minimum execution time. This task can be done on basis of the Ant Colony Optimization technique (ACO) of Swarm Intelligence as it is not deeply studied yet. The main objective of this thesis is to solve the path problem: Means to find the shortest path and Resolve the time problem: Means to minimize the time of finding shortest path. Because of time and cost constraint, it is not possible to perform extensive regression testing. Techniques such as test case selection and prioritization are used to solve the problem of time and cost constraints. In this paper we are modifying the previous technique to get better results in case of execution time and then the Effectiveness of techniques is checked with the help of APFD metric.

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Ants Optimization for Minimal Test Case Selection and Prioritization as to Reduce the Cost of Regression Testing

With the rapid development of information technology, software testing, as a software quality assurance, is becoming more and more important. In the software life cycle, each time the code has changed need to be regression testing. The huge test case library makes running a full test case library being challenged. To this end, we designed a genetic algorithm-based test case prioritization algorithm and improved the genetic algorithm proposed software test case prioritization algorithm.

Test Case Prioritization Technique Based on Genetic Algorithm

The advantages and disadvantages about MATLAB are described in this paper. Several methods of mixed programming between MATLAB and other programming languages such as C#, FORTRAN, JAVA, DELPHI, VB and EXCEL are presented, including using COM Builder, MATLAB Compiler, MATLAB Engine, MATLAB Automation Server, Excel Link and other methods. Finally analysis and suggestions about the occasion adopting these methods are put forward.

Jianyun Chen

Papers

Test Case Prioritization Technique Based on Genetic Algorithm

Mixed Programming between MATLAB and Other Programming Languages