Abstract
Usability is related to software quality, improving its ability to be understood, operated and attractive to users. We proposed the Design Usability Evaluation (DUE) technologies to allow identifying usability problems earlier in the development of Web applications, through the inspection of mockups. While we found that the DUE technique and tool were effective and efficient in the identification of usability problems, we saw the need to investigate their acceptance in practitioners' work environment. This paper reports the results from a study evaluating the acceptance of the DUE technologies from the point of view of software engineers. We asked questions based on the indicators from the Technology Acceptance Model and identified that a majority of the software engineers who participated in the study: (a) found the DUE technologies useful and easy to use for supporting the usability inspection process; and (b) would regularly use the DUE technologies for future inspections in their job. Nevertheless, the practitioners indicated that the technique should be refined in order to reduce the ambiguity and repetition of some of its items, while the tool should become more intuitive.

Abstract
Recommendation systems try to guide the users in carrying out a task providing them with useful information about it. Considering the context of software development, programs are ever-increasing, making it difficult to conduct a detailed verification and validation. Automated static analyzers help to detect possible faults on software products earlier and quickly but, in general, the issue maybe a false-positive warning. In this sense, this work presents and evaluates a recommendation system, called WarningsFIX (WFX), which combines several static analyzers aim at: i) Expand the possible fault domain approached by each static analysis tool increasing the range of warnings types covered, allowing the concentration of a higher number of true-positive warnings. ii) Establish different prioritization strategies of warnings aiming at suggesting for reviewers first analyze the ones with a higher chance of being true-positive. WFX organizes the warnings information via treemaps considering four levels of abstraction: program, package, class, and line the nodes of the treemap on each level may be classified by three different prioritization strategies based on the number of warnings, the number of tools, and the suspicions rate the use of these strategies enables the reviewer to handle the set of warnings in a coordinated way depending on the cost and time constraint available. We perform a feasibility study to evaluate the WFX effectiveness whose results shown that: i) WFX was able to improve the results obtained from combined static analyzers to 44% of the analyzed programs, concentrating for them a greater number of true-positives. ii) WFX, depending on the adopted prioritization strategy, improved from 67.5% to 55% the ranking of lines with real bugs when compared with the list of warnings provided by the automated static analyzers without the WFX support.

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
Code inspection is an important activity to identify defects in the source code and improve software quality. However, even when using techniques such as checklists, inspectors consider implicit decision-making knowledge. In this paper, we perform an exploratory study with groups of inspectors (Group1 and Group2) with two objectives: 1) to present findings on how (and if) auxiliary artifacts interfere in decision making during the code inspection activity, and 2) to show whether there is any influence on the number of defects identified by inspectors when using or not auxiliary artifacts. Both groups used the computational code inspection support of the CRISTA tool, but only Group1 used auxiliary artifacts (requirements, UML diagrams, software metrics). We identified 10 findings. All of them are related to the inspectors’ decision making and the influence of using artifacts on defects identification. The findings suggested that when inspectors use auxiliary artifacts, their effectiveness in identifying defects is improved. Besides, their decision making is more homogeneous than that of inspectors who do not use auxiliary artifacts. However, more investigations are necessary to make the results more generalizable. As future work, different strategies for code inspection techniques can be defined based on the findings.

Abstract
This work presents an empirical study on the relationship between the testers’ profile and their efficiency and preference in the application of tours with tourist metaphor for exploratory software testing. For this purpose, we developed and applied a questionnaire based model to gather as much as possible information about the knowledge, expertise and education level from a group of testers. The results indicated that, in fact, the testers’ profile have impact on the application of tours used in the tourist metaphor: there are differences between the tours preferred by different levels of education and most of testers tend to choose those tours based on what they believed to have the shortest execution time. This work raises a valuable discussion about a humanized process of assigning test tasks in order to improve the efficiency of software testing.