Abstract
The test is a mandatory activity for software quality assurance. The knowledge about the software under testing is necessary to generate high-quality test cases, but to execute more than 80% of its source code is not an easy task, and demands an in-depth knowledge of the business rules it implements. In this article, we investigate the adequacy, effectiveness, and cost of manually generated test sets versus automatically generated test sets for Java programs. We observed that, in general, manual test sets determine higher statement coverage and mutation score than automatically generated test sets. But one interesting aspect recognized is that the automatically generated test sets are complementary to the manual test set. When we combined manual with automated test sets, the resultant test sets overcame in more that 10%, on average, statement coverage and mutation score when compared to the rates of manual test set, keeping a reasonable cost. Therefore, we advocate that we should concentrate the use of manually generated test sets on testing essential and critical parts of the software ACM ISBN.

Abstract
Context. In view of the high impact that the test activity has to the cost of software development and the great popularity of Functional Testing Techniques, it is observed that it is necessary to seek strategies that reduce the cost associated with testing without significant loss of quality. Purpose. To introduce an evolution of the method Functional Systematic Test (TFS), the Functional Test Systematic table-Decision (TFS-TD), and to compare the efficiency and effectiveness of both by using programs that are simple and available on LINUX distributions. Methodology. It is a quantitative, descriptive and case studies research, for the evaluation of TFS and TFS-TD methods and its comparison. Results. Guidelines and an iterative process for the application of TFS-TD were defined. Also, through the case studies, the effectiveness and efficiency of the method was quantified and confronted to the TFS method. Conclusions. The case studies revealed that the TFS-TD presents considerable savings in test cases with minimal loss in quality.

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Abstract
In the previous edition of ISSRE we used a straightforward hunting metaphor to call the attention of the research community to the importance of considering human aspects in software testing - in particular, to research challenges that affect practitioners in the industry. Our focus was on the unit testing level. As a result, we proposed a framework to indicate research directions in three cognitive support dimensions defined to this level of testing. In the present work, we advance in this area considering the visualization domain. We demonstrate an immediate application of our framework to help derive information on how visualizations for unit testing are currently addressing each cognitive support dimension. Our results demonstrate the lack of concern with adequate procedures for both planning and validation of the visualizations regarding cognitive support aspects. These issues affect the capacity of the proposals to support users and put under risk the solutions' reliability.

Abstract
The creation of a suite of unit testing is preceded by the selection of which components (code units) should be tested. This selection is a significant challenge, usually made based on the team member's experience or guided by defect prediction or fault localization models. We modeled the selection of components for unit testing with limited resources as a multi-objective problem, addressing two different objectives: maximizing benefits and minimizing testing cost. To measure the benefit of a component, we made use of metrics from static analysis (cost of future maintenance), dynamic analysis (risk of fault, and frequency of calls), and business value. We tackled gaps and challenges in the literature to formulate an effective method, the Selector of Software Components for Unit Testing (SCOUT). SCOUT provides an automated extraction of all necessary data followed by a multi-objective optimization process. SCOUT is a method able to assist testers in different domains, and the Android platform was chosen to perform our experiments, taking nine leading open-source applications as our subjects. SCOUT was compared with two of the most frequently used strategies in terms of efficacy. We also compared the effectiveness and efficiency of seven algorithms in solving a multi-objective component selection problem. Our experiments were performed under different scenarios, and reveal the potential of SCOUT in reducing the market vulnerability, compared to others approaches. To the best of our knowledge, SCOUT is the first method to assist in an automated way software testing managers in selecting components for the development of unit testing, combining static and dynamic metrics and business value.

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