Abstract
Two issues that have been hampering the widespread adoption of mutation testing are redundant and equivalent mutants. Minimal mutation has been recently introduced to mitigate these two issues by generating and selecting only a subset of non-redundant mutants. Equivalent mutants are syntactically different from the original program, but functionally identical, so it is impossible to come up with test data capable of making equivalent mutants behave differently from the original program under test. In order to mitigate the cost of applying mutation testing, we set out to investigate how machine learning algorithms that generate predictive models can be used to classify mutants as belonging to the minimal set or equivalent. More specifically, we extract a set of features (i.e., properties) from programs, mutants, and test cases, which in turn serve as input to the creation of predictive models. To shed some light on the effectiveness of our approach, we carried out an experiment in which we trained seven different machine learning classifiers, the best of which obtained 81.88% and 80.30% accuracy to classify minimal and equivalent mutants, respectively. Results from our experiment would seem to indicate that our approach can effectively mitigate some of the costs associated with mutation testing by relying on the identification of minimal sets and equivalent mutants.

Abstract
The quality of metamodel considerably affects the models and transformations that conform to it. Despite that, there is still little discussion about a comprehensive form to evaluate the quality of metamodels and its consequences in model-driven development processes. This paper proposes a metamodel quality evaluation framework called MQuaRE (Metamodel Quality Requirements and Evaluation). MQuaRE comprises metamodel quality requirements and measures, a quality model, and an evaluation process, with the evident influence of international standards for software product quality, such as ISO/IEC 25000 series. We present a simple use case of MQuaRE describing how requirements, measures, and the quality model should be used during the evaluation process of a metamodel for software patterns. Among other benefits, MQuaRE can help determine final metamodel quality, decide on the acceptance of a metamodel, and also assess the positive and negative aspects of a metamodel, contributing to its quality evolution.

Abstract
Mutation testing is a testing criterion used to measure the quality of a test suite. In mutation, a test suite is executed against the set of mutants of a given program under testing. A score is computed to measure the adequacy of the test suite in detecting faults. Although powerful, mutation testing has two major drawbacks: The high-computational cost to generate and execute the set of generated mutants and the existence of equivalent mutants. In this paper, we present a preliminary experimental study to investigate the use of control-flow information, aiming to reduce the number of mutants. For this study, only a subset of mutants, defined by its location, is executed. Such location is determined by the set of primitive arcs of a given program under testing. Next, it is analyzed the relationship between minimal mutants and primitive arcs. Results indicate that the approach reduces the number of mutants and equivalent mutants and, in most cases, still maintains a high mutation score concerning full mutation. Moreover, the results also indicate that there is a concentration of minimal mutants on the nodes related to primitive arcs. Finally, we compare the effectiveness of our strategy over random mutant sampling.

Abstract
Testing is a mandatory activity to guarantee software quality. Not only knowledge about the software under testing is required to generate high-quality test cases, but also knowledge about the business rules implemented in software product to cover more than 80% of its source code therefore, we investigate in this study the adequacy, effectiveness, and cost of smart and random automated generated test sets for Java programs. We observed that the smart generated test sets, in general, are more adequate and less expensive than random generated tests, but regarding effectiveness, random generated test are more efficient. Moreover, we observed that smart automated test sets are complementary between them, and we explored if random generated test sets could be complementary to smart automated test sets as well. When we combined smart generated test sets, we observed an increase of more than 8% in statement coverage and more than 15% in mutation score when compared to random generated test sets. However, when we added random generated test sets to previous combination of smart generated test sets, results show a lower increase of statement coverage and mutation score, while increasing considerably the test set generation cost therefore, we advocate that the use of random testing should be integrated with smart generated tests only with a minimization strategy to avoid redundant test sets, keeping the cost reasonable.

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