Multi-objective Test Case Prioritization Research Articles

Fault sensitivity index-based multi-objective testcase prioritization

Abstract Test case prioritization (TCP) is a regression technique that sequences test cases by assigning priority based on specific criteria defined by software testers. Various parameters, such as code coverage, statement coverage, and method coverage, are utilized in Test Case Prioritization (TCP), wherein metaheuristic techniques are widely employed to determine the optimal order of test cases based on these specified parameters. However, simply applying these techniques does not ensure the satisfaction of all the needs of software testers. This paper introduces an empirical study that employs the multi-objective test case prioritization (MOTCP) technique to prioritize the test cases based on target points defined by software testers. The study calculates a Software Complexity Index (SCI) at the code level, identifying fault-prone areas. Furthermore, a Test-case Complexity Index (TCI) is also used for prioritization. The proposed technique incorporates various target points defined by the software tester to calculate SCI and TCI, which serve as our main objectives for TCP. A detailed analysis is also performed to examine the impact of these target points on the generated optimal order of test cases. Finally, the proposed model is compared with other state-of-the-art techniques across various evaluation parameters.

Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm

Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II.

Multi-Objective Test Case Prioritization based on Epistatic Particle Swarm Optimization

Multi-Objective Test Case Prioritization based on Epistatic Particle Swarm Optimization

A Multi-Objective Technique to Prioritize Test Cases

While performing regression testing, an appropriate choice for test case ordering allows the tester to early discover faults in source code. To this end, test case prioritization techniques can be used. Several existing test case prioritization techniques leave out the execution cost of test cases and exploit a single objective function (e.g., code or requirements coverage). In this paper, we present a multi-objective test case prioritization technique that determines the ordering of test cases that maximize the number of discovered faults that are both technical and business critical. In other words, our new technique aims at both early discovering faults and reducing the execution cost of test cases. To this end, we automatically recover links among software artifacts (i.e., requirements specifications, test cases, and source code) and apply a metric-based approach to automatically identify critical and fault-prone portions of software artifacts, thus becoming able to give them more importance during test case prioritization. We experimentally evaluated our technique on 21 Java applications. The obtained results support our hypotheses on efficiency and effectiveness of our new technique and on the use of automatic artifacts analysis and weighting in test case prioritization.

Multi-objective test case prioritization in highly configurable systems: A case study

Test case prioritization schedules test cases for execution in an order that attempts to accelerate the detection of faults. The order of test cases is determined by prioritization objectives such as covering code or critical components as rapidly as possible. The importance of this technique has been recognized in the context of Highly-Configurable Systems (HCSs), where the potentially huge number of configurations makes testing extremely challenging. However, current approaches for test case prioritization in HCSs suffer from two main limitations. First, the prioritization is usually driven by a single objective which neglects the potential benefits of combining multiple criteria to guide the detection of faults. Second, instead of using industry-strength case studies, evaluations are conducted using synthetic data, which provides no information about the effectiveness of different prioritization objectives. In this paper, we address both limitations by studying 63 combinations of up to three prioritization objectives in accelerating the detection of faults in the Drupal framework. Results show that non–functional properties such as the number of changes in the features are more effective than functional metrics extracted from the configuration model. Results also suggest that multi-objective prioritization typically results in faster fault detection than mono-objective prioritization.