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

Practical Combinatorial Interaction Testing: Empirical Findings on Efficiency and Early Fault Detection

Combinatorial interaction testing (CIT) is important because it tests the interactions between the many features and parameters that make up the configuration space of software systems. Simulated Annealing (SA) and Greedy Algorithms have been widely used to find CIT test suites. From the literature, there is a widely-held belief that SA is slower, but produces more effective tests suites than Greedy and that SA cannot scale to higher strength coverage. We evaluated both algorithms on seven real-world subjects for the well-studied two-way up to the rarely-studied six-way interaction strengths. Our findings present evidence to challenge this current orthodoxy: real-world constraints allow SA to achieve higher strengths. Furthermore, there was no evidence that Greedy was less effective (in terms of time to fault revelation) compared to SA; the results for the greedy algorithm are actually slightly superior. However, the results are critically dependent on the approach adopted to constraint handling. Moreover, we have also evaluated a genetic algorithm for constrained CIT test suite generation. This is the first time strengths higher than 3 and constraint handling have been used to evaluate GA. Our results show that GA is competitive only for pairwise testing for subjects with a small number of constraints

Regression test case prioritization by code combinations coverage

Regression test case prioritization (RTCP) aims to improve the rate of fault detection by executing more important test cases as early as possible. Various RTCP techniques have been proposed based on different coverage criteria. Among them, a majority of techniques leverage code coverage information to guide the prioritization process, with code units being considered individually, and in isolation. In this paper, we propose a new coverage criterion, code combinations coverage, that combines the concepts of code coverage and combination coverage. We apply this coverage criterion to RTCP, as a new prioritization technique, code combinations coverage based prioritization (CCCP). We report on empirical studies conducted to compare the testing effectiveness and efficiency of CCCP with four popular RTCP techniques: total, additional, adaptive random, and search-based test prioritization. The experimental results show that even when the lowest combination strength is assigned, overall, the CCCP fault detection rates are greater than those of the other four prioritization techniques. The CCCP prioritization costs are also found to be comparable to the additional test prioritization technique. Moreover, our results also show that when the combination strength is increased, CCCP provides higher fault detection rates than the state-of-the-art, regardless of the levels of code coverage

Factor Selection in Drilling Unidirectional Carbon Fiber Reinforced Plastic Composite Plates with The HSS Drill Bit Using Analytic Hierarchy Process

The present state of competition within the plastic composite industry calls for efficiency to be competitive. However, in the drilling of carbon-fiber-reinforced plastic (CFRP) composites, the process engineer still lacks knowledge of the priority of parameters as parameters are chosen at random, and resources are deployed without justification on their importance and strength. Consequently, production crises and productivity losses persist. In this article, the analytic hierarchy process (AHP) method is deployed to evaluate the weights of criteria in a CFRP composite drilling operation. The establishment of the decision, alternatives, and criteria is accomplished, and pairwise comparisons are conducted to allow the computation of the importance weight of each criterion. The weight is then established. The proposed approach was illustrated with experimental data from the literature with a plastic drilling case. Six criteria were chosen as crucial in determining the drilling parameters of CFRP composites. The results reveal the following: thrust force (0.413), torque (0.253), eccentricity (0.151), surface roughness (0.115), delamination at entry (0.037) and delamination at exit (0.030). In a validation exercise to ascertain the consistency of the analysis, a consistent analysis was obtained. The novelty of the article is using the AHP approach on the drilling of CFRP composites. Practically, these results impact operator training, indicating that attention should be focused on thrust force control. The industrial applications of CFRP composites include the basic structures of automobiles, ships, and airplanes

Automated data integration for developmental biological research

In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research