Abstract
Context: Testing activities are essential for the quality assurance of mobile applications under development. Despite its importance, some studies show that testing is not widely applied in mobile applications. Some characteristics of mobile devices and a varied market of mobile devices with different operating system versions lead to a highly fragmented mobile ecosystem. Thus, researchers put some effort into proposing different solutions to optimize mobile application testing. Objective: The main goal of this paper is to provide a categorization and classification of existing testing infrastructures to support mobile application testing. Methods: To this aim, the study provides a Systematic Mapping Study of 27 existing primary studies. Results: We present a new classification and categorization of existing types of testing infrastructure, the types of supported devices and operating systems, whether the testing infrastructure is available for usage or experimentation, and supported testing types and applications. Conclusion: Our findings show a need for mobile testing infrastructures that support multiple phases of the testing process. Moreover, we showed a need for testing infrastructure for context-aware applications and support for both emulators and real devices. Finally, we pinpoint the need to make the research available to the community whenever possible.

Abstract
Software testing plays a fundamental role in software engineering, involving the systematic evaluation of software to identify defects, errors, and vulnerabilities from the early stages of the development process. Education in software testing is essential for students and professionals, as it promotes quality and favours the construction of reliable software solutions. However, motivating students to learn software testing may be a challenge. To overcome this, educators may incorporate some strategies into the teaching and learning process, such as real-world examples, interactive learning, and gamification. Gamification aims to make learning software testing more engaging for students by creating a more enjoyable experience. One approach that has proven effective is to use serious games. This paper presents a novel serious game to teach white-box testing test case design techniques, named GAMFLEW (GAMe For LEarning White-box testing). It describes the design, game mechanics, and its implementation. It also presents a preliminary evaluation experiment with students to assess the usability, learnability, and perceived problems, among other aspects. The results obtained are encouraging.

Abstract
Scientific research in digital transformation is expanding in scope, quantity, and relevance, bringing forth diverse perspectives on which factors and specific dimensions-such as organizational structure, culture, and technological readiness-affect the success of digital transformation initiatives. Numerous studies have proposed mechanisms to assess an organization's maturity through digital transformation across various models. Some of these models focus on external influences, others on internal factors, or both. Although these assessments provide valuable insights into a company's transformation state, they often lack consistency, and recent research highlights key gaps. Specifically, many models primarily reflect the views of senior management on the general progress of digital transformation rather than on measurable outcomes. Moreover, these models tend to target large enterprises, overlooking small and medium enterprises (SMEs), which are crucial to economic growth yet face unique challenges, such as limited resources and expertise. Our study addresses these gaps by concentrating on SMEs and introducing a novel approach to assessing digital transformation readiness-a metric that reflects how prepared an organization is to optimize transformation outcomes. Following design science research methodology, we develop a model that centers on the perspectives of general employees, offering companies an in-depth view of their readiness across 20 dimensions. Each dimension is evaluated through behaviors indicative of the highest level of digital transformation readiness, helping companies identify areas to maximize potential benefits. Our model focuses not on technological quality but on the degree to which behaviors essential for leveraging technology and innovative business models are integrated within the organization.

Abstract
The purpose of this study is to ascertain the hierarchical importance of a patent's characteristics to licensing. This research has a causal-exploratory purpose, in that it sought to establish relationships between variables. This research aims to identify which characteristics are influential in the licensing of Brazilian academic patents in the biotechnology and pharmaceutical technology fields, based on the mining of data contained in licensed and unlicensed patent documents. Which characteristics of Brazilian academic patents are most influential in their licensing potential? An analysis through Random Forest was performed. To the best of our knowledge, there are no studies in Brazil using machine learning to identify which characteristics are influential in licensing a particular academic patent, especially given the difficulty of gathering this information. We found that regardless of the measure used, the three most critical licensing characteristics for the Biotechnology and Pharmaceutical patents analysed are Patent Scope, Life Cycle, and Claims. At the same time, the least important is the Patent Cooperation Treaty. The relevance of this research is based on the fact that after identifying which intrinsic characteristics influence the final value and licensing probabilities of a given patent, it will be possible to develop mathematical models that provide accurate information for establishing technology transfer agreements. In practical terms, the results suggest that greater patent versatility, combined with lifecycle management and a technical effort to build strong claims, increases the licensing potential of academic biopharmaceutical patents.

Abstract
Code comprehension is often supported by source code analysis tools that provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multilanguage pattern detection tool that uses the multilanguage capability of the LARA framework to support finding pattern instances in a code base. LARA provides a virtual AST, which is common to multiple OOP programming languages, and DP-LARA then performs code analysis of detecting pattern instances on this abstract representation. We evaluate the detection performance and consistency of DP-LARA with a few software projects. Results show that a multilanguage approach does not compromise detection performance, and DP-LARA is consistent across the languages we tested it for (i.e., Java and C/C++). Moreover, by providing a virtual AST as the abstract representation, we believe to have decreased the effort of extending the tool to new programming languages and maintaining existing ones.

Abstract
Mutation testing maybe used to guide test case generation and as a technique to assess the quality of test suites. Despite being used frequently, mutation testing is not so commonly applied in the mobile world. One critical challenge in mutation testing is dealing with its computational cost. Generating mutants, running test cases over each mutant, and analyzing the results may require significant time and resources. This research aims to contribute to reducing Android mutation testing costs. It implements mutation testing operators (traditional and Android-specific) according to mutant schemata (implementing multiple mutants into a single code file). It also describes an Android mutation testing framework developed to execute test cases and determine mutation scores. Additional mutation operators can be implemented in JavaScript and easily integrated into the framework. The overall approach is validated through case studies showing that mutant schemata have advantages over the traditional mutation strategy (one file per mutant). The results show mutant schemata overcome traditional mutation in all evaluated aspects with no additional cost: it takes 8.50% less time for mutant generation, requires 99.78% less disk space, and runs, on average, 6.45% faster than traditional mutation. Moreover, considering sustainability metrics, mutant schemata have 8,18% less carbon footprint than traditional strategy.