Context: Executing an entire regression test-suite after every code change is often costly in large software projects. To cope with this challenge, researchers have proposed various regression test-selection techniques. Objective: This paper was motivated by a real industrial need to improve regression-testing practices in the context of a safety-critical industrial software in the defence domain in Turkey. To address our objective, we set up and conducted an "action-research" collaborative project between industry and academia. Method: After a careful literature review, we selected a conceptual multi-objective regression-test selection framework (called MORTO) and adopted it to our industrial context by developing a custom-built genetic algorithm (GA) based on that conceptual framework. GA is able to provide full coverage of the affected (changed) requirements while considering multiple cost and benefit factors of regression testing. e.g., minimizing the number of test cases, and maximizing cumulative number of detected faults by each test suite. Results: The empirical results of applying the approach on the Software Under Test (SUT) demonstrate that this approach yields a more efficient test suite (in terms of costs and benefits) compared to the old (manual) test-selection approach, used in the company, and another applicable approach chosen from the literature. With this new approach, regression selection process in the project under study is not ad-hoc anymore. Furthermore, we have been able to eliminate the subjectivity of regression testing and its dependency on expert opinions. Conclusion: Since the proposed approach has been beneficial in saving the costs of regression testing, it is currently in active use in the company. We believe that other practitioners can apply our approach in their regression-testing contexts too, when applicable. Furthermore, this paper contributes to the body of evidence in regression testing by offering a success story of successful implementation and application of multi-objective regression testing in practice.

Purpose Wind turbines are one of the best candidates to solve the problem of increasing energy demand in the world. The aim of this paper is to apply a multi-objective structural optimization study to a Phase II wind turbine blade produced by the National Renewable Energy Laboratory to obtain a more efficient small-scale wind turbine. Design/methodology/approach To solve this structural optimization problem, a new Non-Dominated Sorting Genetic Algorithm (NSGA-II) was performed. In the optimization study, the objective function was on minimization of mass and cost of the blade, and design parameters were composite material type and spar cap layer number. Design constraints were deformation, strain, stress, natural frequency and failure criteria. ANSYS Composite PrepPost (ACP) module was used to model the composite materials of the blade. Moreover, fluid–structure interaction (FSI) model in ANSYS was used to carry out flow and structural analysis on the blade. Findings As a result, a new original blade was designed using the multi-objective structural optimization study which has been adapted for aerodynamic optimization, the NSGA-II algorithm and FSI. The mass of three selected optimized blades using carbon composite decreased as much as 6.6%, 11.9% and 14.3%, respectively, while their costs increased by 23.1%, 29.9% and 38.3%. This multi-objective structural optimization-based study indicates that the composite configuration of the blade could be altered to reach the desired weight and cost for production. Originality/value ACP module is a novel and advanced composite modeling technique. This study is a novel study to present the NSGA-II algorithm, which has been adapted for aerodynamic optimization, together with the FSI. Unlike other studies, complex composite layup, fiber directions and layer orientations were defined by using the ACP module, and the composite blade analyzed both aerodynamic pressure and structural design using ACP and FSI modules together.

The title compound, C15H15NO3, adopts the enol–imine tautomeric form. The two rings are twisted with respect to each other, making a dihedral angle of 44.08 (5)°. The 3-methoxy-2-[(E)-(4-methoxyphenyl)-iminomethyl]phenol unit is almost planar, the largest deviation from the mean plane being 0.047 (2) Å. Such a planar conformation might be related to the occurrence of an intra­molecular O—H⋯N hydrogen bond. In the crystal, inter­molecular C—H⋯O hydrogen bonds link the mol­ecules into sheets parallel to (010). These sheets are inter­connected by weak C—H⋯π inter­actions.

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