Geolocation emerges as an important tool in the surveillance and control of vector-borne diseases such as dengue, chikungunya, Zika, and urban yellow fever. In contrast to manual control methods, which are slow and prone to errors, geolocation can be utilized. This work incorporates the use of geolocation in a project that develops a technological solution for entomological surveillance and vector control of arboviruses, aiming to develop a computational system for registering this information. The main objectives of this work include the development and user validation of the geolocation data collection functionality during the registration of infestation sites, and the integration of relevant application features, such as the geographic registration of city blocks. Throughout the execution of the work, user-centered evaluations were carried out, involving agents from the State Health Department. This work contributes to accurately identifying arbovirus infestation points through geolocation, adding precision to the agent's geographic validation, and providing visual aid in route creation.

Arboviruses such as dengue, Zika, and chikungunya remain a serious public health challenge in Brazil and other Latin American countries. Effective surveillance and vector control require integrated information systems to support planning and monitoring efforts. This study presents a heuristic evaluation of a web-based system developed for arbovirus control coordinators in Brazil, aiming to identify interface limitations and propose improvements based on user experience. The Design Science Research Methodology (DSRM) guided the study, and a structured questionnaire grounded in Nielsen’s Heuristics was applied. A total of 72 undergraduate students from technology-related programs participated in the evaluation. Each participant performed guided tasks within the system and provided feedback through both closed and open-ended questions. Results indicated positive perceptions regarding interface consistency and organization, while usability issues were identified in areas such as error prevention, flexibility, and feedback mechanisms. Statistical analysis revealed differences among participant groups, highlighting the influence of user profiles on perceived usability. The findings underscore the value of heuristic evaluation in improving public health digital tools and demonstrate the potential for adapting such systems to similar regional contexts across Latin America.

This study investigates the use of gamification as an active methodology in Software Engineering education, aiming to enhance engagement, motivation, and learning effectiveness. The research proposes guidelines to assist educators in planning gamified activities, covering aspects such as scenario definition, player profiles, and game elements. The guidelines were validated through an exploratory study with teachers, who evaluated their effectiveness, ease of use, and adaptability to the educational context. Results indicate that the guidelines facilitate the integration of gamification in teaching. While validated with a reduced sample size and focused on Software Engineering, the findings highlight their potential for broader interdisciplinary application, offering a practical resource to foster more dynamic and engaging learning experiences.

The adoption of microservice architecture has seen a considerable upswing in recent years, mainly driven by the need to modernize legacy systems and address their limitations. Legacy systems, typically designed as monolithic applications, often struggle with maintenance, scalability, and deployment in-efficiencies. This study investigates the motivations, activities, and challenges associated with migrating from monolithic legacy systems to microservices, aiming to shed light on common practices and challenges from a practitioner's point of view. We conducted a comprehensive study with 53 software practitioners who use mi-croservices, expanding upon previous research by incorporating diverse international perspectives. Our mixed-methods approach includes quantitative and qualitative analyses, focusing on four main aspects: (i) the driving forces behind migration, (ii) the ac-tivities to conduct the migration, (iii) strategies for managing data consistency, and (iv) the prevalent challenges. Thus, our results reveal diverse practices and challenges practitioners face when migrating to microservices. Companies are interested in technical benefits, enhancing maintenance, scalability, and deployment processes. Testing in microservice environments remains complex, and extensive monitoring is crucial to managing the dynamic nature of microservices. Database management remains challenging. While most participants prefer decentralized databases for autonomy and scalability, challenges persist in ensuring data consistency. Additionally, many companies leverage modern cloud technologies to mitigate network overhead, showcasing the importance of cloud infrastructure in facilitating efficient microservice communication.

Refactoring is a non-trivial maintenance activity. Developers spend time and effort refactoring code to remove structural problems, i.e., code smells. Recent studies indicated that developers often apply composite refactoring (composite, for short), i.e., two or more interrelated refactorings. However, prior studies revealed that only 10% of composite refactorings are considered complete, i.e., those fully removing code smells. Many incomplete refactorings can even replace or introduce smells, requiring additional effort for their removal later in the project. Moreover, existing refactoring recommendations are not well-detailed and do not alert developers about these possible side effects. To address these gaps, we conducted a large-scale study involving more than 250k refactorings from 42 software projects, including both open-source and closed-source projects. Our goal is to investigate how the most common complete composites are combined and their side effects in the practice. Our results reveal that the current recommendation to apply Extract Method(s) with fine-grained refactoring types needs refinements. We found that certain fine-grained refactorings like Change Variable Types and Change Return Types can introduce up to 45% of Brain Methods when combined with Extract Method(s). Moreover, Ex-tract Method(s) and Move Method(s), a common recommendation to remove Feature Envy, may inadvertently introduce about 30% of Lazy Classes and approximately 70% of Data Classes. Despite these potential side effects, existing refactoring catalogs and tools' recommenders do not alert developers about these side effects. Finally, we consolidate our findings into a catalog to provide clear guidance for developers and researchers on effectively applying composite refactorings to fully remove code smells.

The modernization of monolithic legacy systems with microservices has been a trend in recent years. As part of this modernization, identifying microservice candidates starting from legacy code is challenging, as maintainers may consider many criteria simultaneously. Multi-objective search-based approaches represent a promising state-of-the-art solution to support this decision-making process. However, the rationale to adopt each microservice candidate automatically identified by these approaches is poorly investigated in industrial cases. Furthermore, studies with these approaches have not carefully investigated how maintainers reason and make decisions when designing microservice architectures from legacy systems. To address this gap, we conducted an on-site case study with maintainers of an industrial legacy system to investigate the usefulness of automatically generated microservice architectures. We analyze design decisions pointed out by the maintainers when reasoning about microservice candidates using several criteria at the same time. Our study is the first to assess a search-based approach involving actual maintainers conceiving microservice architectures in an industrial setting. Therefore, firstly, we considered individual evaluation of microservice candidates to understand the rationale for identifying a service. Secondly, we conducted a focus group study with maintainers with the goal of investigating design decisions at an architectural level. The results show that:(i)the automated approach is able to identify useful microservices;(ii)the criteria observed by previous studies are, in fact, considered by maintainers; and(iii)the maintainer profiles, i.e., the preferred granularity for microservice, highly affect design decisions. Finally, we observed the maintainers needed little effort in adjusting the automatically identified microservices to make them adoptable. In addition to indicating a promising potential of search-based approaches to generate microservice architectures, our findings highlight the need for:(i)interactive and/or customizable approaches that enable maintainers to include their preferences during the search process, and(ii)flexible or automated selection of criteria that fits the scenario in which the modernization is taking place.

Microservices are small and autonomous services that communicate through lightweight protocols. Companies have often been adopting microservices to incrementally redesign legacy systems as part of a modernization process. Microservices promote better reuse and customization of existing features while increasing business capabilities, if appropriate design decisions are made. There are some partially-automated approaches supporting the re-design of legacy features into microservices. However, they fail in covering two key aspects: (i) provide an architectural design of the features being redesigned, and (ii) simultaneously support relevant criteria, e.g., feature modularization and decrease of network communication overhead. Also, these two aspects tend to be poorly discussed along industrial case studies. To fulfill these gaps, we propose a redesign strategy to support the re-engineering of features legacy code as microservices. This strategy covers key possibly-conflicting criteria on microservice-based architectures. We employ search-based optimization to deal with such conflicting criteria. The output of the strategy is a set of redesign candidates of legacy features as microservices. We reflect upon the benefits and drawbacks of the proposed strategy through an industrial case study. In particular, we perform an in-depth analysis of the resulting microservice candidates, and a discussion about their potential for customization and reuse. The reflections/discussions are also supported by observations of developers involved in the process.

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA designing has been formulated as a multi-objective optimization problem and successfully solved by a state-of-the-art search-based approach. However, the majority of empirical studies optimize PLA designs without applying one of the fundamental genetic operators: the crossover. An operator for PLA design, named Feature-driven Crossover, was proposed in a previous study. In spite of the promising results, this operator occasionally generated incomplete solutions. To overcome these limitations, this paper aims to enhance the search-based PLA design optimization by improving the Feature-driven Crossover and introducing a novel crossover operator specific for PLA design. The proposed operators were evaluated in two well-studied PLA designs, using three experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results show the usefulness and efficiency of the presented operators on reaching consistent solutions. We also observed that the two operators complement each other, leading to PLA design solutions with better feature modularization than the baseline experiment.

The expensive maintenance of legacy systems leads companies to migrate such systems to microservice architectures. This migration requires the identification of system's legacy parts to become microservices. However, the successful identification of microservices, which are promising to be adoptable in practice, requires the simultaneous satisfaction of many criteria, such as coupling, cohesion, reuse and communication overhead. Search-based microservice identification has been recently investigated to address this problem. However, state-of-the-art search-based approaches are limited as they only consider one or two criteria (namely cohesion and coupling), possibly not fulfilling the practical needs of developers. To overcome these limitations, we propose toMicroservices, a many-objective search-based approach that considers five criteria, the most cited by practitioners in recent studies. Our approach was evaluated in a real-life industrial legacy system undergoing a microservice migration process. The performance of toMicroservices was quantitatively compared to a baseline. We also gathered qualitative evidence based on developers' perceptions, who judged the adoptability of the recommended microservices. The results show that our approach is both: (i) very similar to the most recent proposed approach on optimizing the traditional criteria of coupling and cohesion, but (ii) much better when taking into account all the five criteria. Finally, most of the microservice candidates were considered adoptable by practitioners.

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an optimization problem with many factors. In this context, the MOA4PLA approach was proposed to optimize PLA design using search algorithms and metrics specific to the context. MOA4PLA treats the PLA design as a multi-objective optimization problem. At the end of the search process several PLA design alternatives are presented to the decision maker, difficulting the decision about which PLA alternative best fits with his/her needs. In this sense, this work proposes the usage of clustering algorithms to group PLAs design alternatives, according to their characteristics and assist the decision maker in the choice of one PLA design for the SPL. For this purpose, an empirical study was carried out, involving quantitative and qualitative experiments. Such an study integrated the K-Means++ and DBSCAN clustering algorithms in the MOA4PLA approach. The results of the experiments were promising, since an appropriate grouping of the solutions can be quantitatively observed, and also, qualitatively, the suitability of the solutions to the decision makers needs was verified.

Root canal refactoring is a software development activity that is intended to improve dependability-related attributes such as modifiability and reusability. Despite being an activity that contributes to these attributes, deciding when applying root canal refactoring is far from trivial. In fact, finding which elements should be refactored is not a cut-and-dried task. One of the main reasons is the lack of consensus on which characteristics indicate the presence of structural degradation. Thus, we evaluated whether the density and diversity of multiple automatically detected symptoms can be used as consistent indicators of the need for root canal refactoring. To achieve our goal, we conducted a multi-case exploratory study involving 6 open source systems and 2 systems from our industry partners. For each system, we identified the classes that were changed through one or more root canal refactorings. After that, we compared refactored and non-refactored classes with respect to the density and diversity of degradation symptoms. We also investigated if the most recurrent combinations of symptoms in refactored classes can be used as strong indicators of structural degradation. Our results show that refactored classes usually present higher density and diversity of symptoms than non-refactored classes. However, root canal refactorings that are performed by developers in practice may not be enough for reducing degradation, since the vast majority had little to no impact on the density and diversity of symptoms. Finally, we observed that symptom combinations in refactored classes are similar to the combinations in non-refactored classes. Based on our findings, we elicited an initial set of requirements for automatically recommending root canal refactorings.

The architectural properties of product‐line architecture (PLA) design have been successfully optimised by multi‐objective genetic algorithms (GAs). Memetic algorithms (MAs) extend GA by adding a local search after the global search process. MA outperformed GA in the context of class modelling, software testing and the next release problem. However, no studies on the application of MAs for PLA design optimisation were found in the literature. In light of this, the authors performed an exploratory study, where MA was used to apply design patterns for a search‐based PLA design (SBPD) and achieved promising results. From the obtained results, they adjusted the MA‐based implementation. This study aims at investigating if the MA is more profitable to SBPD than GAs. Two empirical studies that involve four PLA designs were carried out for this task by varying the pair of objective functions. Empirical results show that MA achieved satisfactory solutions, despite being influenced by the original PLA design.

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an optimization problem with many factors. In this context, OPLA-Tool was developed to automatically identify the best alternatives for a PLA design using multi-objective evolutionary algorithms, based on genetic algorithms (GA). From an original PLA, OPLA-Tool obtains alternative designs to improve the original one in terms of the objectives selected for optimization. In a recent study, we extend OPLA-Tool to add a memetic algorithm (MA) and promising empirical results were obtained. However, the results allow us to hypothesize that including feature modularization as an objective to be optimized by MA could obtain even better solutions. In addition, it is interesting to know the experts' opinion about PLA designs automatically obtained, what has not been done yet. Thus, the objective of this work is twofold: to investigate the aforementioned hypothesis and to conduct the first qualitative evaluation of the PLA design solutions automatically obtained by search algorithms. An empirical study involving MA and GA was carried out with four different PLA designs. MA presented the best solutions according the quality indicators used in the quantitative analysis. The results of the qualitative evaluation showed that the optimized solutions are well evaluated by the experts. After both analyzes, the hypothesis could be confirmed.

The adoption of the layered architectural style can improve the Product Line Architecture (PLA) design by providing a better organization of the elements, flexibility and maintainability. Search based optimization approaches can also benefit the PLA design, by generating PLA alternatives associated with the best trade-offs between different measures (fitness) related to cohesion, coupling and feature modularization. However, the usage of existing search operators changes the PLA organization, and consequently may violate layered rules, impacting negatively in the architecture understanding. In order to solve this problem, this work introduces search operators that consider the layered architectural style rules. Results show that the proposed operators contribute to obtain better solutions, maintaining the adopted style, with better or equivalent fitness values.

The Product Line Architecture (PLA) design is a complex task, influenced by many factors such as feature modularization and PLA extensibility, which are usually evaluated according to different metrics. Hence, the PLA design is an optimization problem and problems like that have been successfully solved in the Search-Based Software Engineering (SBSE) area, by using metaheuristics such as Genetic Algorithm. Considering this fact, this paper introduces a tool named OPLA-Tool, conceived to provide computer support to a search-based approach for PLA design. OPLA-Tool implements all the steps necessary to use multi-objective optimization algorithms, including PLA transformations and visualization through a graphical interface. OPLA-Tool receives as input a PLA at the class diagram level, and produces a set of good alternative diagrams in terms of cohesion, feature modularization and reduction of crosscutting concerns.

The Product Line Architecture (PLA) design is a multi-objective optimization problem that can be properly solved in the Search Based Software Engineering (SBSE) field. However, the PLA design has specific characteristics. For example, the PLA is designed in terms of features and a highly modular PLA is necessary to enable the growth of a software product line. However, existing search based design approaches do not consider such needs. To overcome this limitation, this paper introduces a feature-driven crossover operator that aims at improving feature modularization. The proposed operator was applied in an empirical study using the multi-objective evolutionary algorithm named NSGAII. In comparison with another version of NSGAII that uses only mutation operators, the feature-driven crossover version found a greater diversity of solutions (potential PLA designs), with higher feature-based cohesion, and less feature scattering and tangling.

The Product Line Architecture (PLA) design is a multi-objective optimization problem that can be properly solved with search-based algorithms. However, search-based PLA design is an incipient research field. Due to this, works in this field have addressed main points to solve the problem: adequate representation, specific search operators and suitable evaluation fitness functions. Similarly what happens in the search-based design of traditional software, existing works on search-based PLA design use NSGA-II, without evaluating the characteristics of this algorithm, such as the use of crossover operator. Considering this fact, this paper reports results from a comparative analysis of two algorithms, NSGA-II and PAES, to the PLA design problem. PAES was chosen because it implements a different evolution strategy that does not employ crossover. An experimental study was carried out with nine PLAs and results of the conducted study attest that NSGA-II performs better than PAES in the PLA design context.

The Product Line Architecture (PLA) can be improved by taking into account key factors such as feature modularization, and by continuously evaluating its design according to metrics. Search-Based Software Engineering (SBSE) principles can be used to support an informed-design of PLAs. However, existing search-based design works address only traditional software design not considering intrinsic Software Product Line aspects. This paper presents MOA4PLA, a search-based approach to support the PLA design. It gives a multi-objective treatment to the design problem based on specific PLA metrics. A metamodel to represent the PLA and a novel search operator to improve feature modularization are proposed. Results point out that the application of MOA4PLA leads to PLA designs with well modularized features, contributing to improve features reusability and extensibility. It raises a set of solutions with different design trade-offs that can be used to improve the PLA design.

A product-line architecture (PLA) entails a design reused by a family of products sharing several features. The long-term stability of a PLA largely depends on the modularization of each feature from the design outset. As many features of a product-line crosscut the PLA decomposition, their modularity and stability are often improved with aspect-orientation according to recent studies. However, the use of this technique in proactive design of PLAs is challenging and has rarely been investigated. The problem is that information about the crosscutting nature of features is limited in this case. This paper presents a first study aimed at analyzing the feasibility of deriving stable aspect-oriented PLAs through a proactive design method. Our study relies on a product line and presents an in-depth exploratory analyse. We compare the stability of aspect-oriented and alternative candidates for proactively-designed PLAs. The results indicate that it is possible to derive aspect-oriented PLAs with superior stability based on a proactive design method. In addition, it is possible to anticipate pros and cons of PLAs with aspects when the latter are incorporated at an early design stage.

The Product-Line Architecture (PLA) is the main artifact of a Software Product Line (SPL). However, obtaining a modular, extensible and reusable PLA is a people-intensive and non-trivial task, related to different and possible conflicting factors. Hence, the PLA design is a hard problem and to find the best architecture can be formulated as an optimization problem with many factors. Similar Software Engineering problems have been efficiently solved by search-based algorithms in the field known as Search-based Software Engineering. The existing approaches used to optimize software architecture are not suitable since they do not encompass specific characteristics of SPL. To easy the SPL development and to automate the PLA design this work introduces a multi-objective optimization approach to the PLA design. The approach is now being implemented by using evolutionary algorithms. Empirical studies will be performed to validate the neighborhood operators, SPL measures and search algorithms chosen. Finally, we intend to compare the results of the proposed approach with PLAs designed by human architects.

During the inter-class test, a common problem, named Class Integration and Test Order (CITO) problem, involves the determination of a test class order that minimizes stub creation effort, and consequently test costs. The approach based on Multi-Objective Evolutionary Algorithms (MOEAs) has achieved promising results because it allows the use of different factors and measures that can affect the stubbing process. Many times these factors are in conflict and usually there is no a single solution for the problem. Existing works on MOEAs present some limitations. The approach was evaluated with only two coupling measures, based on the number of attributes and methods of the stubs to be created. Other MOEAs can be explored and also other coupling measures. Considering this fact, this paper investigates the performance of two evolutionary algorithms: NSGA-II and SPEA2, for the CITO problem with four coupling measures (objectives) related to: attributes, methods, number of distinct return types and distinct parameter types. An experimental study was performed with four real systems developed in Java. The obtained results point out that the MOEAs can be efficiently used to solve this problem with several objectives, achieving solutions with balanced compromise between the measures, and of minimal effort to test.