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A Unified Test Case Prioritization Approach
"... Test case prioritization techniques attempt to re-order test cases in a manner that increases the rate at which faults are detected during regression testing. Coverage-based test case prioritization techniques typ-ically use one of two overall strategies, a total strategy or an additional strategy. ..."
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Test case prioritization techniques attempt to re-order test cases in a manner that increases the rate at which faults are detected during regression testing. Coverage-based test case prioritization techniques typ-ically use one of two overall strategies, a total strategy or an additional strategy. These strategies prioritize test cases based on the total number of code (or code-related) elements covered per test case and the number of additional (not-yet-covered) code (or code-related) elements covered per test case, respectively. In this ar-ticle, we present a unified test case prioritization approach that encompasses both the total and additional strategies. Our unified test case prioritization approach includes two models (“basic ” and “extended”) by which a spectrum of test case prioritization techniques ranging from a purely total to a purely additional technique can be defined by specifying the value of a parameter referred to as the fp value. To evaluate our approach, we performed an empirical study on 28 Java objects and 40 C objects, considering the impact of three internal factors (model type, choice of fp value, and coverage type) and three external factors (cover-age granularity, test case granularity, and programming/testing paradigm), all of which can be manipulated by our approach. Our results demonstrate that a wide range of techniques derived from our basic and ex-tended models with uniform fp values can outperform purely total techniques, and are competitive with purely additional techniques. Considering the influence of each internal and external factor studied, the
To Be Optimal Or Not in Test-Case Prioritization
"... Abstract—Software testing aims to assure the quality of software under test. To improve the efficiency of software testing, especially regression testing, test-case prioritization is proposed to schedule the execution order of test cases in software testing. Among various test-case prioritization te ..."
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Abstract—Software testing aims to assure the quality of software under test. To improve the efficiency of software testing, especially regression testing, test-case prioritization is proposed to schedule the execution order of test cases in software testing. Among various test-case prioritization techniques, the simple additional coverage-based technique, which is a greedy strategy, achieves surprisingly competitive empirical results. To investigate how much difference there is between the order produced by the additional technique and the optimal order in terms of coverage, we conduct a study on various empirical properties of optimal coverage-based test-case prioritization. To enable us to achieve the optimal order in acceptable time for our object programs, we formulate optimal coverage-based test-case prioritization as an integer linear programming (ILP) problem. Then we conduct an empirical study for comparing the optimal technique with the simple additional coverage-based technique. From this empirical study, the optimal technique can only slightly outperform the additional coverage-based technique with no statistically significant difference in terms of coverage, and the latter significantly outperforms the former in terms of either fault detection or execution time. As the optimal technique schedules the execution order of test cases based on their structural coverage rather than detected faults, we further implement the ideal optimal test-case prioritization technique, which schedules the execution order of test cases based on their detected faults. Taking this ideal technique as the upper bound of test-case prioritization, we conduct another empirical study for comparing the optimal technique and the simple additional technique with this ideal technique. From
PORA: Proportion-Oriented Randomized Algorithm for Test Case Prioritization
"... Abstract—Effective testing is essential for assuring software quality. While regression testing is time-consuming, the fault detection capability may be compromised if some test cases are discarded. Test case prioritization is a viable solution. To the best of our knowledge, the most effective test ..."
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Abstract—Effective testing is essential for assuring software quality. While regression testing is time-consuming, the fault detection capability may be compromised if some test cases are discarded. Test case prioritization is a viable solution. To the best of our knowledge, the most effective test case prioritization approach is still the additional greedy algorithm, and existing search-based algorithms have been shown to be visually less effective than the former algorithms in previous empirical studies. This paper proposes a novel Proportion-Oriented Randomized Algorithm (PORA) for test case prioritization. PORA guides test case prioritization by optimizing the distance between the priori-tized test suite and a hierarchy of distributions of test input data. Our experiment shows that PORA test case prioritization tech-niques are as effective as, if not more effective than, the total greedy, additional greedy, and ART techniques, which use code coverage information. Moreover, the experiment shows that PORA techniques are more stable in effectiveness than the others. Index Terms—Test case prioritization, randomized algorithm, proportional sampling strategy, multi-objective optimization I. OVERVIEW A.
A Genetic Algorithm Based Average Percentage of Statement Coverage Technique for Test Case Ordering
"... Abstract: This paper address the research in the field of test case ordering in regression testing. The idea is to improve APSC by applying our proposed approach adaptive genetic algorithm hybrid approach for test case ordering in regression testing. In this research basically we focused on test-cas ..."
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Abstract: This paper address the research in the field of test case ordering in regression testing. The idea is to improve APSC by applying our proposed approach adaptive genetic algorithm hybrid approach for test case ordering in regression testing. In this research basically we focused on test-case ordering and statement coverage by Applying adaptive genetic algorithm hybrid approach and measure APSC (Average Percentage Statement Coverage) and GA (Genetic Algorithm). In this research we take hundred test-case of apache server and evaluate hundred test-cases. We used java eclipse environment for coding and run the test cases. First we apply APSC (Average Percentage of statement coverage) technique for ordering test-cases as well measure the APSC. We got good results but this technique not sufficient to cover maximum statement. So, we hybrid the adaptive and Genetic Algorithm approach to measure the APSC and run all test-cases until all statement not covered.. Our approach gives us better results than single APSC adaptive technique.
Regression Testing based on Hamming Distance and Code Coverage
"... A software testing process that tries to uncover new bugs for an existing system from the previous test suite due to expansion of the software is known as Regression Testing. The test suite will hold the same test cases that were tested for the system in its earlier version. For regression testing, ..."
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A software testing process that tries to uncover new bugs for an existing system from the previous test suite due to expansion of the software is known as Regression Testing. The test suite will hold the same test cases that were tested for the system in its earlier version. For regression testing, prioritizing the test cases is always a complex as well as challenging task. In fact researchers have been proposing many approaches to arrange the test cases so that the cost of the software can be reduced in terms of human labor, time, and money as well. Many such approaches have shown quite good results too. In this paper, we have proposed a new approach of prioritizing the test cases that extends hamming distance based prioritization with code coverage based techniques. Our proposed method helps to unfold the previous bugs as well as the newly arrived bugs at the early cycle of the regression testing. Keywords:
