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Composite materials are obtained by combining two or more materials with different physical properties, which maintain their properties at the macromolecular level, thus retaining their identity, to obtain a resulting material with characteristics clearly superior to the constituent elements, comparable to conventional materials in terms of performance. The specimens are made either by injection molding, machined from the center sections of a standard test sample (see ISO 20735: 2018), processed from semifinished, finished, rolled, or extruded products. In this sense, the article briefly presents the activity of creating a modeling, simulation. and tensile testing software program that can acquire control and process data, taken from an experimental stand or from a software simulation. Thus, the following elements are realized: software for retrieving data from the software equipment of the Zwick/Roell Z020 traction test machine; taking data from an experimental stand; simulation of tensile testing; creating reports, management of the production logistics chain of the benchmark. The experimental and theoretical results are further input in the design of the presented software. [ABSTRACT FROM AUTHOR]

Software testing is crucial in continuous integration (CI). Ideally, at every commit, all the test cases should be executed, and moreover, new test cases should be generated for the new source code. This is especially true in a Continuous Test Generation (CTG) environment, where the automatic generation of test cases is integrated into the continuous integration pipeline. In this context, developers want to achieve a certain minimum level of coverage for every software build. However, executing all the test cases and, moreover, generating new ones for all the classes at every commit is not feasible. As a consequence, developers have to select which subset of classes has to be tested and/or targeted by test‐case generation. We argue that knowing a priori the branch coverage that can be achieved with test‐data generation tools can help developers into taking informed decision about those issues. In this paper, we investigate the possibility to use source‐code metrics to predict the coverage achieved by test‐data generation tools. We use four different categories of source‐code features and assess the prediction on a large data set involving more than 3'000 Java classes. We compare different machine learning algorithms and conduct a fine‐grained feature analysis aimed at investigating the factors that most impact the prediction accuracy. Moreover, we extend our investigation to four different search budgets. Our evaluation shows that the best model achieves an average 0.15 and 0.21 MAE on nested cross‐validation over the different budgets, respectively, on EVOSUITE and RANDOOP. Finally, the discussion of the results demonstrate the relevance of coupling‐related features for the prediction accuracy. [ABSTRACT FROM AUTHOR]