Abstract test case prioritization using repeated small-strength level-combination coverage

Abstract—Abstract Test Cases (ATCs) have been widely used in practice, including in combinatorial testing and in software product line testing. When constructing a set of ATCs, due to limited testing resources in practice (for example in regression testing), Test Case Prioritization (TCP) has been proposed to improve the testing quality, aiming at ordering test cases to increase the speed with which faults are detected. One intuitive and extensively studied TCP technique for ATCs is λ-wise Level-combination Coverage based Prioritization (λLCP), a static, black-box prioritization technique that only uses the ATC information to guide the prioritization process. A challenge facing λLCP, however, is the necessity for the selection of the fixed prioritization strength λ before testing — testers need to choose an appropriate λ value before testing begins. Choosing higher λ values may improve the testing effectiveness of λLCP (for example, by finding faults faster), but may reduce the testing efficiency (by incurring additional prioritization costs). Conversely, choosing lower λ values may improve the efficiency, but may also reduce the effectiveness. In this paper, we propose a new family of λLCP techniques, Repeated Small-strength Level-combination Coverage-based Prioritization (RSLCP), that repeatedly achieves the full combination coverage at lower strengths. RSLCP maintains λLCP’s advantages of being static and black box, but avoids the challenge of prioritization strength selection. We performed an empirical study involving five different versions of each of five C programs. Compared with λLCP, and Incremental strength LCP (ILCP), our results show that RSLCP could provide a good trade-off between testing effectiveness and efficiency. Our results also show that RSLCP is more effective and efficient than two popular techniques of Similarity-based Prioritization (SP). In addition, the results of empirical studies also show that RSLCP can remain robust over multiple system releases

Prioritising abstract test cases: an empirical study

Test-case prioritisation (TCP) attempts to schedule the order of test-case execution such that faults can be detected as quickly as possible. TCP has been widely applied in many testing scenarios such as regression testing and fault localisation. Abstract test cases (ATCs) are derived from models of the system under test and have been applied to many testing environments such as model-based testing and combinatorial interaction testing. Although various empirical and analytical comparisons for some ATC prioritisation (ATCP) techniques have been conducted, to the best of the authors' knowledge, no comparative study focusing on the most current techniques has yet been reported. In this study, they investigated 18 ATCP techniques, categorised into four classes. They conducted a comprehensive empirical study to compare 16 of the 18 ATCP techniques in terms of their testing effectiveness and efficiency. They found that different ATCP techniques could be cost-effective in different testing scenarios, allowing us to present recommendations and guidelines for which techniques to use under what conditions. © The Institution of Engineering and Technology 2018

On the selection of strength for fixed-strength interaction coverage based prioritization

Abstract test cases are derived by modeling the system under test, and have been widely applied in practice, such as for software product line testing and combinatorial testing. Abstract test case prioritization (ATCP) is used to prioritize abstract test cases and aims at achieving higher rates of fault detection. Many ATCP algorithms have been proposed, using different prioritization criteria and information. One ATCP approach makes use of fixed-strength level-combinations information covered by abstract test cases, and is called fixed-strength interaction coverage based prioritization (FICBP). Before using FICBP, the prioritization strength λ needs to be decided. Previous studies have generally focused on λ values ranging between 1 and 6. However, no study has investigated the appropriateness of such a range, nor how to assign the prioritization strength for FICBP. To answer these questions, this paper reports on an empirical study involving four real-life programs (each of which with six versions). The experimental results indicate that λ should be set approximately equal to a value corresponding to half of the number of parameters, when testing resources are sufficient. Our results also show that when testing resources are limited or insufficient, either small or large λ values are suggested for FICBP