Context: Traditional awareness programs on privacy and information security in the public sector are often perceived as unengaging and ineffective in promoting lasting behavioral change. Human error remains a key vulnerability, and there is growing interest in more engaging approaches such as gamification. Objective: This study investigates whether gamification can enhance privacy and information security awareness among public servants, and identifies the organizational factors that influence its perceived feasibility. Method: We conducted a quantitative survey with 518 Brazilian public servants working in Information and Communications Technology (ICT), using descriptive and inferential statistical analysis, including non-parametric tests and ordinal logistic regression, to analyze perceptions of traditional and gamified approaches, engagement elements, and institutional readiness. Results: Respondents rated traditional methods as moderately effective but lacking in engagement. Gamification was perceived as more motivating and effective for knowledge retention, and as having the potential to support awareness and perceived behavioral improvement. Key adoption barriers included organizational culture and lack of understanding, while leadership support and institutional encouragement of innovation emerged as the main enablers. Multivariate analysis further shows that organizational factors are the strongest predictors of perceived feasibility, while prior experience with gamification also has a significant effect and individual knowledge-related characteristics do not. Conclusion: Gamification is perceived as a promising and feasible strategy for improving awareness training in the public sector. However, its successful adoption depends on overcoming cultural resistance and ensuring institutional support. The findings provide empirical evidence to guide future implementations and inform the design of privacy and security training policies.

Abstract Context: Java source code may inadvertently embed personally identifiable information (PII), creating privacy, security, and regulatory compliance risks in contemporary software engineering pipelines. Objective: We empirically compare three architectural strategies for entity-level PII detection in Java source code: a classifier-only pipeline based on transformer models, a hybrid classifier plus large language model (LLM) pipeline in which an open-weight LLM judges classifier-generated candidates, and an LLM-centered structured-extraction pipeline with deterministic validation and sanitization. Method: We evaluate the three pipelines on a synthetic Java dataset using shared preprocessing, value-level matching, and micro-averaged precision, recall, and F $$_1$$ -score. The experiments use controlled configurations, fixed prompt templates, deterministic post-processing, and archived artifacts to support reproducibility and auditability. Results: Individual classifier baselines showed limited recall in isolation, while union-based ensemble aggregation increased coverage at the cost of additional false positives. The hybrid architecture reduced some false positives, but also systematically removed baseline true positives, producing precision-oriented filtering behavior that prioritizes selectivity over coverage. In contrast, the LLM-centered structured-extraction pipeline achieved the most favorable precision–recall balance among the evaluated strategies when structured-output compliance was stable and deterministic sanitization was enforced. Conclusions: The results indicate that architectural integration strategies and pipeline constraints substantially shape PII detection behavior, often beyond the isolated choice of individual models. Under controlled conditions, open-weight LLMs can support privacy-oriented source code analysis when embedded in reproducible, auditable, and validation-aware pipelines that explicitly monitor recall loss and structured-output robustness.

Abstract This study empirically assesses how effectively two Large Language Models (LLMs), and , can transform terse feature-request titles from open-source software (OSS) issue trackers into well-formed software requirements. It further examines how prompt-engineering strategies shape requirement quality and evaluates a scalable “LLM-as-a-Judge” approach for automated quality assessment based on three ISO/IEC/IEEE 29148:2018 quality attributes: Unambiguity , Verifiability , and Singularity . We extract 150 feature-request titles from five OSS repositories and pair each title with every combination of two LLMs and three prompt styles, producing 900 candidate requirements. An independent evaluator LLM rates each requirement on these three quality attributes using a Likert scale and provides textual rationales. Ordinal data are analyzed with descriptive statistics and non-parametric tests, complemented by thematic analysis of the evaluator’s explanations. A targeted human validation study with five evaluators on a stratified sample of 50 requirements assesses the reliability of the automated judge. LLMs often produce high-quality requirements; however, scores vary with input clarity and prompt design. Few-shot prompting consistently boosts Singularity , while Expert Identity prompting sometimes raises Verifiability but frequently harms Singularity . shows a modest but significant edge in Singularity . Qualitative review echoes these patterns, revealing trade-offs between added detail and focus. The LLM-as-a-Judge protocol delivers consistent, scalable evaluations whose scores correlate significantly with aggregate human judgment ( $$p < 0.05$$ for all three attributes). Modern LLMs can expedite the drafting of initial software requirements from informal OSS inputs, but their output quality hinges on careful prompt selection and the inherent clarity of the source title. Prompt effects are model-dependent and may introduce trade-offs among quality attributes, so human oversight remains indispensable for refinement. The LLM-as-a-Judge framework proves a practical, human-validated technique for large-scale evaluation, enabling rapid, reproducible insights into LLM-driven requirements engineering workflows.

Ensuring data privacy is a major challenge for software developers, especially in chatbots, where balancing privacy protection with response quality is key, given the need for conversation-driven development and data protection regulations. This research identifies privacy requirements and techniques for chatbot development through a literature review, privacy policy analysis, and a practitioner survey. The methodology includes a Systematic Literature Review (SLR), an adapted Gray Literature Review (GLR), privacy requirement formulation, and validation via a survey. Based on the SLR and GLR, eight privacy requirements are proposed, covering personal information protection, user authentication, access control, secure communication, database safety, user rights empowerment, decentralized storage, and reliable infrastructure. Survey results highlight foundational measures like secure communication and scalable infrastructures as priorities, while advanced measures such as decentralized storage or privacy rights implementation scored lower due to complexity and cost. Practitioners also stressed clarity and verifiability, citing gaps in definitions, examples, and validation criteria as challenges to adoption.

The conversational nature of chatbots poses challenges to designers since their development is different from other software and requires investigating new practices in the context of human-AI interaction and their impact on user experience. Therefore, this work aims to unveil text-based chatbot conversational practices alongside their impacts on users to build a web guide to support designers while conceiving conversations for chatbots. We have carried out a Systematic Literature Review (SLR) to identify linguistic, visual, and interactive elements used in text-based chatbot conversational design. The SLR resulted in 40 selected studies that were reviewed and coded into a set of conversational guidelines that were evaluated through a survey. Respondents strongly agreed that applying the proposed guidelines in chatbot development would produce greater user satisfaction and user engagement and that the guide is usable, flexible, clear, and understandable, making it a great ally in building text-based chatbots with an improved user experience.

Abstract Design Thinking techniques have been widely used in software requirements elicitation to understand the necessities of stakeholders and end‐users. However, there is a lack of evidence of their effectiveness when applied to guide the development process of a system targeting vulnerable populations. What are the implications of using Design Thinking techniques to elicit requirements in a community of former inmates—and what would be the benefits of and challenges in this deployment? In this paper, we report our experience of using Design Thinking for requirements elicitation of a mobile application customized for the former inmates of the Brazilian prison system and their families. We applied techniques such as Brainstorming, Stakeholder Mapping, Personas Creation, Rapid Ethnography, and Interviews to obtain relevant data and create several prototypes. These techniques contribute to the development of an uncommon application that aims to help the reintegration process of former inmates into society. Our results validate the initial hypothesis that such techniques, when applied to a sensitive context, assist product development that meets the end‐users' needs by creating a higher quality product. The main limitation of the research was the lack of access to low‐literacy end‐users and/or former inmates without previous experience using mobile devices.

Contribution: The main contribution presented by this work was proposing a method for building gamification projects that can be replicated in teaching applied computing, developing new software solutions, or creating gamification projects for other areas. The exposed methodology not only proposes a solution to engage people in favor of learning objectives effectively but also presents two distinct gamification approaches using the same framework that can be used separately or together depending on the complexity of the application scenario. The implementation of this methodology can contribute to the advancement of knowledge in the field of gamification and motivation, enabling future studies to analyze the effectiveness of the proposed techniques and improve engagement strategies. Background: There are various contexts in which spontaneous user action is necessary or desirable, such as exercising the right to vote in a democracy or attending an algebra class. In both cases, motivation can be crucial in how an individual engages in these activities. Self-determination theory suggests that both extrinsic and intrinsic motivation can promote such engagement. Gamification, through motivational elements known as game techniques, allows for creating an immersive environment for specific activities. The focus is on adapting the approach to achieve a goal that is aligned with the motivational and behavioral profile of the participants. Intended outcomes: This approach was employed in a real-life case to propose a gamification solution that motivates participating lawyers in trial sessions of a Brazilian higher court to provide spontaneous, contextualized, neutral, and frequent feedback. Application design: This work proposes the development of a gamification project by merging two assessments based on the Octalysis gamification framework: one to identify motivation techniques based on expert opinions in the field of study and another to identify motivation techniques by mapping the motivational factors of the participant persona using self-determination theory. The final gamification project is the proposal of Octalysis techniques for each core driver resulting from the merging of the assessments. Findings: The proposed gamification project resulted in 16 techniques distributed among 7 core drivers of the Octalysis framework. At the end of the project, the techniques to be used in the gamification design are presented. The proposed approach, based on merging assessments and applying the Octalysis framework, can be applied in different contexts and activities, always to increase engagement and motivation among the individuals involved.

Background: Several researchers report the impact of gender on software development teams, especially in relation to women. In general, women are under-represented on these teams and face challenges and difficulties in their workplaces. When it comes to women who are mothers, these challenges can be amplified and directly impact these women’s professional lives, both in industry and academia. However, little is known about women ICT practitioners’ perceptions of the challenges of maternity in their professional careers. Objective: This paper investigates mothers’ challenges and difficulties in global software development teams. Method: We conducted a survey with women in the ICT field who work in academia and global technology companies. We surveyed 141 mothers from different countries and employed mixed methods to analyze the data. Results: Our findings reveal that women face sociocultural challenges, including work-life balance issues, bad jokes, and moral harassment. The prejudices they suffer make them insecure and with low confidence in the work performed. Furthermore, they usually do not have a supporting network during and after maternity leave, which culminates in them feeling overloaded. The surveyed women suggested a set of actions to reduce the challenges they face in their workplaces, such as: creating a code of conduct for men and childcare within companies. Conclusion: Women face many challenges when they become mothers. Our findings explore these challenges and can help organizations in developing policies to minimize them. Also, it can help raise awareness of co-workers and bosses, toward a more friendly and inclusive workplace.

Requirements elicitation is a fundamental step in a software development process since it is at this stage that the software begins to be designed. In some situations, the problems related to the failure of the software development project are due to an incomplete requirements elicitation, resulting in solutions that do not understand all the necessary functionalities or do not incorporate innovation. Despite the various techniques offered by Requirements Engineering, situations such as the growing application market and the need for innovation further increase the importance of understanding the user’s different needs. In this paper, we investigated how the journey map and personas techniques are being used in requirements elicitation in both the literature and the industry, along with the advantages, disadvantages and challenges of using these techniques. We conducted systematic literature review to identify the personas and journey map techniques used in requirements elicitation in the literature and industry. In addition, we conducted a survey with 52 practitioners (software developers, users and managers) to investigate their perceptions of the use of journey maps and personas techniques in the requirements elicitation phase. Twenty-four primary studies were identified that address journey map and personas techniques in software requirements elicitation. In addition, most respondents stated that using these techniques facilitates understanding the requirements, providing better integration, collaboration and leveling of knowledge among the members of the software development teams. Our findings allow us to conclude that most of the software developers, users and managers that participated in the survey consider that the journey map and personas techniques are effective in helping understand the software requirements to be developed by the development teams.

Misuse of cryptographic (crypto) APIs is a noteworthy cause of security vulnerabilities. For this reason, static analyzers were recently proposed for detecting crypto API misuses. They differ in strengths and weaknesses, and they might miss bugs. Motivated by the inherent limitations of static analyzers, this article reports on a study of runtime verification (RV) as a dynamic-analysis-based alternative for crypto API misuse detection. RV monitors program runs against formal specifications; it was shown to be effective and efficient for amplifying the bug-finding ability of software tests. We focus on the popular JCA crypto API and write 22 RV specifications based on expert-validated rules in a static analyzer. We monitor these specifications while running tests in five benchmarks. Lastly, we compare the accuracy of our RV-based approach, RVSec, with those of three state-of-the-art crypto API misuses detectors: CogniCrypt, CryptoGuard, and CryLogger. Results show that RVSec has higher accuracy in four benchmarks and is on par with CryptoGuard in the fifth. Overall, RVSec achieves an average${\boldsymbol{F}}_{1}$measure of 95%, compared with 83%, 78%, and 86% for CogniCrypt, CryptoGuard, and CryLogger, respectively. We highlight the strengths and limitations of these tools and show that RV is effective for detecting crypto API misuses. We also discuss how static and dynamic analysis can complement each other for detecting crypto API misuses.

Context: The use of Creativity and Design Thinking (C&DT) techniques favor the generation of new ideas based on the needs of users and stakeholders, and can support software developers during the process of requirements elicitation. Objectives: In this work, we aim to identify C&DT techniques to perform requirements elicitation proposed in the literature and in the industry and investigate the perception of software developers about using these techniques. Methods: We conducted a systematic literature review (SLR) to identify the C&DT techniques in the literature and a regional survey with software development teams from several companies in Brazil to identify which techniques found in the literature are currently being used by organizations. The survey also investigated the level of knowledge that software developers have regarding the C&DT techniques, and whether they agree that the use of these techniques can help to achieve a more effective process of requirements elicitation. Results: In the SLR, we identified 86 C&DT techniques that support requirement elicitation activities. In the survey, most developers outlined that C&DT techniques facilitate requirements elicitation and stated that they have more knowledge and usage experience with DT techniques than creativity techniques. The most used DT techniques mentioned by survey participants were: interview, brainstorming, uses cases, activity analysis, user story, and rapid prototyping, whereas for creativity techniques were: analogies, creativity workshops, focus group, questions list, clarification, none and combining ideas. Conclusions: The results showed that despite the existence of a large number of techniques in the literature, the developers’ lack of knowledge about these techniques makes them not used in the industry. However, the developers’ responses showed that the use of C&DT techniques helps to make requirements elicitation more effective.

The Brazilian General Data Protection Law (LGPD) implementation has impacted activities carried out by the software development teams. Due to it, developers had to become aware of the existing techniques and tools to carry out privacy requirements elicitation. Extending our previous work, we have investigated the actions taken by organizations regarding the LGPD, specifically in software development, considering the perception of agile development teams after two years of the LGPD implementation. In addition, we also investigated the perception of an agile team regarding the practices, techniques, and tools previously cited by practitioners as potential solutions for use in this context, along with techniques already in use in the current context. We have conducted a systematic literature review (SLR) and selected 36 primary studies. Furthermore, we have conducted a survey with 53 IT practitioners and semi-structured interviews with ten practitioners. The LGPD principles are known by most agile teams and are being implemented by the organizations, although the existing tools to support privacy requirements elicitation are still underused by agile teams. Moreover, agile teams consider that software requirements and software construction are the most impacted areas of knowledge by the LGPD, and most of them use user stories in privacy requirements elicitation. Our findings reveal that agile teams and Brazilian organizations are more concerned with user data privacy issues after the LGPD became effective. However, agile teams still face challenges in privacy requirements elicitation.

Context: The Database Systems (DS) subject is a significant subject in Computer Science and Engineering-related undergraduate programs, as it supplies knowledge and abilities required for professional practice within the software industry. Teaching DS principles, concepts, and practices and relating them to real-world scenarios is challenging, particularly in social isolation circumstances due to Covid-19. Moreover, teaching resources can pose additional setbacks for teachers and students who are unfamiliar with remote learning. Purpose: This study investigates the DS students' perception regarding the methodology and teaching practices adopted by the teacher during the social isolation period. Method: We conducted a survey with the DS subject students to investigate their perception regarding the teaching methodology adopted, the challenges, and their motivation during this period of social isolation. Results: Our findings revealed that most students have adequate study conditions, i.e., a suitable environment for studying at home and the necessary equipment to attend online classes and perform the subject's practical tasks. Moreover, 79% of the students felt motivated to study the subject remotely, and more than 74% of the students endorsed the teaching methodology adopted by the teacher.

Context: The implementation of the Brazilian General Data Protection Law (LGPD) may impact activities carried out by the software development teams. It is necessary for developers to know the existing techniques and tools to carry out privacy requirements elicitation. Objectives: In this research, we investigated the perception of agile software development team members from different organizations, regarding the impact that LGPD will have on the activities of the software development process. Methods: We conducted an online survey and a systematic literature review to identify the techniques, methodologies and tools used in the literature to perform privacy requirements elicitation in the context of Agile Software Development (ASD). In addition, we also investigated the perception of an agile team from a Federal Public Administration organization regarding the impacts of the obligation to develop software in accordance with the LGPD. Results: Our findings reveal that agile teams know the concepts related to data privacy legislation, but they do not use the techniques proposed in the literature to perform privacy requirements elicitation. In addition, agile teams face problems with outdated software requirements specifications and stakeholders’ lack of knowledge regarding data privacy. Conclusions: Agile teams need to improve their knowledge on privacy requirements.

Background. The effect of gender diversity in open source communities has gained increasing attention from practitioners and researchers. For instance, organizations such as the Python Software Foundation and the OpenStack Foundation started actions to increase gender diversity and promote women to top positions in the communities. Problem. Although the general underrepresentation of women (a.k.a. horizontal segregation) in open source communities has been explored in a number of research studies, little is known about the vertical segregation in open source communities---which occurs when there are fewer women in high level positions. Aims. To address this research gap, in this paper we present the results of a mixed-methods study on gender diversity and work practices of core developers contributing to open-source communities. Method. In the first study, we used mining-software repositories procedures to identify the core developers of 711 open source projects, in order to understand how common are women core developers in open source communities and characterize their work practices. In the second study, we surveyed the women core developers we identified in the first study to collect their perceptions of gender diversity and gender bias they might have observed while contributing to open source systems. Results. Our findings show that open source communities present both horizontal and vertical segregation (only 2.3% of the core developers are women). Nevertheless, differently from previous studies, most of the women core developers (65.7%) report never having experienced gender discrimination when contributing to an open source project. Finally, we did not note substantial differences between the work practices among women and men core developers. Conclusions. We reflect on these findings and present some ideas that might increase the participation of women in open source communities.

This Innovative Practice Full Paper presents a model that includes ways to propose and construct software artifacts concerned with User Experience issues in Project-Based Learning (PBL) semesters long units. Project-Based Learning has been a trend in conducting teaching practices in modules related to software development. One of the cornerstones of Project-Based Learning is authenticity, a principle that guides the creation of software and development in the classroom by grounding it in a real-world context. However, the authenticity as addressed today in Project-Based Learning for software development, while recognizing that it is important to have quality in the end-user artifact, does not explicitly include User Experience (UX) in its scope. Creating software products that consider UX can have several advantages for students who create them and for a community that will benefit from these systems. This paper presents a proposal for a Project-Based Learning model that uses UX in the scope of projects. This model was built based on theoretical evidence found in a systematic literature review and practical evidence found in three exploratory case studies conducted during the development of this work. The purpose of the model is to increase the acceptance rate of the software developed by the students and help them in creating a portfolio for seeking their first job. The advantages and limitations of the model are discussed and reported. The results indicate that the model is suitable and can be used in any software development discipline and related areas.

Autism spectrum is a disorder that offers a lot of difficulties and challenges to people with it and related to. As for the learning process of these children, it is known that the use of consistent repetitions helps in the learning of people with autism and, in addition, that combinations of images with words are one of the main means of aid in learning for autistic people. Augmented Reality (AR) is a digital resource that acts as a way of visualizing virtual elements, often three-dimensional (3D), together with the real environment. AR offers "enormous educational potential" when used in conjunction with gamification. This work aims to implement a gamified project using augmented reality to assist in the learning process of children with Autism Spectrum Disorder (ASD). The project was implemented in "Cri.Ativa", the psycho-pedagogical care room of a child development center that provides multidisciplinary care for children with atypical development (especially for children on the autism spectrum) in Palmas, Brazil. In general, it is considered that the objective of the work was achieved because the desired process was gamified and the results obtained by its implementation were positive. However, it is important to highlight that, due to the huge number of possible characteristics for children with ASD, this result does not demonstrate that the application of the described process will have equally positive outcomes in all contexts.

This Research Full Paper studies the impact of using Calango language in the introductory learning of algorithms and computer programming by Brazilian engineering undergraduates. Calango is an educational multi-platform tool designed to facilitate algorithm learning and provide a simple syntax that enables developing advanced logic, so that students concentrate on logic without worrying about language details, considering its based on Portuguese pseudocode commands. A survey was conducted to evaluate if the teaching methods and tools used in the engineering introductory computer programming course were helpful. The questionnaire was applied at the end of four consecutive semesters of lecturing the course, and five questions were selected to analyze Calango's influence in the learning process. Approximately 95% of the participants agreed that Calango should continue in the class. Students reported that the learning of C language was more accessible due to the initial contact with Calango, mainly because they could write code in Portuguese, their native language. Overall results show that Calango was well-received by students, and it eased their early programming learning.

Due to the popularization of Android and the full range of applications (apps) targeting this platform, many security issues have emerged, attracting researchers and practitioners' attention. As such, many techniques for addressing security Android issues have emerged, including approaches for mining sandboxes using dynamic analysis tools (i.e., automated testing tools). Undoubtedly, the resulting sandboxes' efficiency depends on the test case generation tools used in the mining procedures. Previous research studies have compared Android test case generation tools for this specific goal. However, it is difficult to increment the research in this field because reproducing these previous empirical studies is a challenging and time-consuming task. This difficulty occurs because it is necessary to integrate test generation tools that often require different and conflicting versions of the Android platform, programming languages (e.g., Python 2 and Python 3), and software libraries. To mitigate this issue, in this paper we present DroidXP, a software infrastructure that allows researchers (and tools developers) to integrate and compare test case generation tools for mining sandboxes. We evaluated DroidXP through a reproduction study of previous research work, though considering additional test case generation tools. Our experiment suggests that DroidXP simplifies the comparison of existing tools for mining sandboxes, and revealed that Sapienz outperforms the other test case generation tools-regardless of the Monkey tool had presented the highest code coverage in our study.

Static analysis tools are frequently used to detect common programming mistakes or bad practices. Yet, the existing literature reports that these tools are still underused in the industry, which is partly due to (1) the frequent high number of false positives generated, (2) the lack of automated repairing solutions, and (3) the possible mismatches between tools and workflows of development teams. In this study we explored the question: “How could a bot-based approach allow seamless integration of static analysis tools into developers' workflows?” To this end we introduce C-3PR, an event-based bot infrastructure that automatically proposes fixes to static analysis violations through pull requests (PRs). We have been using C-3PR in an industrial setting for a period of eight months. To evaluate C-3PR usefulness, we monitored its operation in response to 2179 commits to the code base of the tracked projects. The bot autonomously executed 201346 analyses, yielding 610 pull requests. Among them, 346 (57%) were merged into the projects' code bases. We observed that, on average, these PRs are evaluated faster than general-purpose PRs (2.58 and 5.78 business days, respectively). Accepted transformations take even shorter time (1.56 days). Among the reasons for rejection, bugs in C-3PR and in the tools it uses are the most common ones. PRs that require the resolution of a merge conflict are almost always rejected as well. We also conducted a focus group to assess how C-3PR affected the development workflow. We observed that developers perceived C-3PR as efficient, reliable, and useful. For instance, the participants mentioned that, given the chance, they would keep using C-3PR. Our findings bring new evidence that a bot-based infrastructure could mitigate some challenges that hinder the wide adoption of static analysis tools.

This paper presents an analysis of data from the National Fund of Education Development (FNDE) in relation to the teachers in its database, relating them according to the demographic density of each region, the number of teachers from the Brazilian Capitals who have courses graduate, specialization, master's and doctoral degrees and the percentages of the Development index of basic education of the students (IDEB) of the respective regions. The goal is to analyze if the academic qualification of the teachers impact on the IDEB of the schools students of the regions with more representative indexes. In our verification we used the Apriori association algorithm to identify and analyze the data set to collect the results. The results demonstrate that the maximum academic qualification of male teachers is mostly of specialization, whereas female teachers in most of the regions have a specialization and master's degree course. Furthermore, it was possible to verify that the region with the highest IDEB is the South region with 6.27%, the region with the highest number of teachers with a master degree (4.2%) and a doctor (0.5%). The Central West region has an IDEB of 6.20% and the professors have the majority of the undergraduate course (56.7%) and specialization (31.9%). It is possible to affirm with the results obtained that the regions with the highest IDEB are the regions with the highest level of training by the teachers.

This article aims at expanding the understanding of the use of quality requirements in line with the classification elements contained in ISO/IEC 25010 sub-characteristics. An analysis was made of 26 systems in a large-size public financial institution, including systems used outside and inside the organization. We used content analysis and the NVivo software to categorize and analyze 942 quality requirements. Considering that the quality requirements chosen were different in their levels of abstraction, detail, and completeness, criteria were defined to allow the classification. Based on the result obtained, it was found that a large part of the previously defined requirements follows the line of classification of the standard, in spite of their having different terms. The analysis also identified requirements that follow a common standard. It was also possible to identify the following high interest characteristics: Usability (31%), Performance Efficiency (20%), and Maintainability (15%). The Functional suitability and Compatibility characteristics had a smaller percentage of attention, each one with less than 3% of requirements identified.

In this Research to Practice Full Paper we investigate whether the application of metrics and indicators can contribute to monitor and verify if a project is performing better when developed using the Design Thinking methodology. Furthermore, we investigate the most appropriate indicators to establish this measurement. To this end, we applied a questionnaire with ten questions, in order to verify the interviewees conceptual level about what is Design Thinking and what are the indicators. From the results, we sought to identify the perception of the interviewees about the use of indicators in Design Thinking, in a more profound way. The questionnaires were carried out in the e-survey format and the analyzes were carried out taking into account the interviewee level of education and the level of knowledge that he/she had in relation to Design Thinking and indicators. The results demonstrated the difficulty of defining specific indicators for projects that use Design Thinking since most of the interviewees did not demonstrate minimal conceptual knowledge regarding the methodology and the Design Thinking process, as well as the definition and application of indicators. However, it was possible to analyze a list of indicators as possible candidates for the application in projects that use Design Thinking, considering that few people have demonstrated to have the minimum conceptual knowledge on the subject and, therefore, this small group of people can not reflect the general context about what are the best indicators for measuring steps in the Design Thinking process.

The Brazilian education methodology follows a very traditional pattern in the middle and high school years. Although it is very difficult to measure the degree of study between particular and public schools, the first-year university students face many difficulties between the clash of very different teaching learning methods. This paper presents an analysis of Competitive Programming, a non-traditional approach based on the Algorithm Problem Solving method, applied to teaching first-year programming classes for the Computer Science course at the University of Brasília (UnB). The main goal is to analyze if there is any correlation between the different teaching methods applied to this specific subject and an increased proficiency toward abilities that are useful not merely to programming, but also to group related projects in their future, such as proactivity and familiarity towards programming logic. The leading method of data acquiring was utilizing a questionnaire and the analysis by data provided by teachers, which brought an in-depth exploration about a disparity between the student's difficulty towards more advanced subjects when introduced with the two different teaching methods, and an improvement in both student's proactivity and familiarity regarding programming.

The use of automatic static analysis tools (ASATs) has gained increasing attention in the last few years. Even though available research have already explored ASATs issues and how they are fixed, these studies rely on revisions of the software, instead of mining real usage of these tools and real issue reports. In this paper we contribute with a comprehensive, multi-method study about the usage of SonarQube (a popular static analysis tool), mining 421,976 issues from 246 projects in four different instance of SonarQube: two hosted in open-source communities (Eclipse and Apache) and two hosted in Brazilian government institutions (Brazilian Court of Account (TCU) and Brazilian Federal Police (PF)). We first surveyed team leaders of the analyzed projects and found that they mostly consider ASATs warning messages as relevant for overall software improvement. Second, we found that both Eclipse and TCU employ highly customized instance of SonarQube, with more than one thousand distinct checkers-though just a subset of these checkers actually led to issues' reports. Surprisingly, we found a low resolution rate per project in all organizations-on average, 13% of the issues have been solved in the systems. We conjecture that just a subset of the checkers reveal real design and coding flaws, and this might artificially increase the technical debt of the systems. Nevertheless, considering all systems, there is a central tendency(median) of fixing issues after 18.99 days they had been reported, faster than the period for fixing bugs as reported in previous studies.

This paper presents the scenario of the Engineering courses of the Faculty UnB Gama (FGA) in relation to the number of male newcomers versus the number of female ones. It is possible to see, through analysis of the data collected, that the number of male students is much higher than that of female ones, from the creation of the campus in the second semester of 2008 until now. This occurs despite FGAs efforts in promoting the Engineering courses on campus using an experimental laboratory, through the Girls in Computing Program, supported by the National Council of Scientific and Technologic Development (CNPq), in partnership with the high schools of the region. Lectures are also conducted, focused on the female public, and looking do debunk the idea that Engineering is a typically male-oriented course, and trying to awaken the vocational interest of women towards Engineering. Through the surveys focused in the low rate of women in engineering courses, some things come up: (1) the lack of stimuli from family and friends when they intend to graduate in this field, and (2), in a way, the stigmatized role of women in Engineering.

Collaborative development approaches (e.g., pair programming, coding dojo, and hackathons) have gained increasing attention in recent years, mostly because they help to share knowledge during software development activities and might shorten development cycles and increase the quality of software products. Collaborative development approaches bring also the potential benefit to contribute to learning activities. For instance, novices might participate on collaborative development sessions in order to learn new development practices, tools, and techniques used in a software development project. Besides these potential benefits, little is known about the perception of students engaged in collaborative development efforts. Therefore, in this paper we investigate whether or not the engagement of students in collaborative development efforts contributes to the learning process of software development practices and techniques, as well as the perceived benefits and challenges related to collaborative software development activities. To this end, we first performed several of coding dojo sessions during a period of 18 months. These development sessions have been conducted within the context of a real software modernization effort, which aims to modernize two enterprise systems of the Brazilian Army. After that, we carried out a qualitative study where the participants (students, software developers, and software architects) answered a survey, in order to understand the learning benefits of using coding dojo in software development activities. The results so far are encouraging. Coding Dojos allowed professors and software architects to seamless share their experience in software development with the students. According to the answers, the methodology created a better environment for the team, allowing better discussions and ideas to be shared and implemented. This has helped the team members to solve problems easier than by themselves, bringing additional benefits, such as steep the learning curve in programming languages, usage of development tools, understanding the requirements, and code refactoring.

In recent years, agile methods of software development have gained a lot of attention in the field of software engineering.Several estimation techniques have been proposed by several authors and developers in recent years.This paper performs a Systematic Literature Review aiming to identify the most used metrics e/or methods in the development of agile software and the most used size metrics regarding effort estimates, deadlines and costs in a planning of agile software project.The results suggest that Planning Poker is the most popular technique for agile teams in the planning phase, Story Point and Point of Function are the most used metrics in agile projects for estimating size, time, effort, productivity and cost.

To meet socio-cultural demands imposed by the new millennium and its implications for education, it was necessary for teachers and students to participate actively in the teaching-learning process. In this context, the classes' evaluation by students has been a tool widely used in various universities in different countries. For institutions and teachers, these assessments allow knowing and measuring the results, further analysis of the institutional status, reviewing projects, adjusting goals, diagnosing weaknesses and correcting possible deviations. For the monitoring and improvement of this process, it was necessary to create instruments that aimed to evaluate the performance of the proposed changes. The teaching-learning methodology adopted in the Introduction to Computer Science classes may be a process that makes it difficult to understand the principles of language programming for undergraduate students in Computer Science and related areas, generating high failure and course drop out rates. This paper presents an analysis of the results obtained in the Introduction to Computer Science classes taught in five Engineering courses, of the Faculty of Gama (FGA) at University of Brasília (UnB). It was analyzed the evaluation questionnaire answered by the undergraduate students in 2017, perform validation of the questionnaire, check the level of students satisfaction in relation to the evaluated subject and the association among the level of satisfaction, the percentage of practical activities of the discipline, student performance and the level of absenteeism. 519 responses were studied, 63% of students reported satisfaction with the classes taken, however a significant number of students 37% reported dissatisfaction with assessment practices and the dynamics of the classes.

This paper presents a methodology to identify the students' gamification profiles for educational projects. Gamification is the application of game elements and game principles in non-game contexts. We propose a process to identify profiles in gamification through the use of ludic activities and gamification driven questionnaires. The choice of activities follows some information of the target audience, such as: age range, cell phone use, internet access and other data. The use of questionnaires is necessary to cross-check the activity data, analyze data and identify the resulting gamification profile. The proposed methodology consists of six steps process: making a baseline, defining core drivers, defining activities, preparing a questionnaire, planning data collection, and analyzing data. The purpose of the process is to identify which gamification core drivers (the motivations) may be most effective in attracting students' attention. Gamification is a powerful tool to attract students. Also, a good gamification rise their interest up to learn and caught attention for a long period of time. A good gamification educational project will produce a very attractive educational game. The resulting game will motivate students to do something different: to study in a new way. We used Yu Kai-Chou's Octalysis framework to identify and model the student profile. The final gamification project is also an Octalysis model. The Octalysis framework is based on basic human motivations according to eight core drivers, which are: Epic Meaning & Calling, Development & Accomplishment, Empowerment of Creativity & Feedback, Ownership & Possession, Social Influence & Relatedness, Scarcity & Impatience, Unpredictability & Curiosity and Loss & Avoidance. Finally, in order to test and evaluate the methodology, we applied the whole process on four classes of two different public schools. The 98 students are from different ages and different grades (7th to 10th grades). The results indicate the process is useful to identify the students' gamification profile, the activities and the questionnaires provide a good data set to identify the core drivers and the resulting gamification project clearly depicts the final profile.

In this paper, we describe and test a part of an active learning environment that is being used to engage and empower students' learning in a undergraduate engineering course. We developed a survey system, called Milsa, in order to insert questions and template answers, and to automatically correct the questions based on template answers. In Milsa, the students insert multiple questions and template answers, the questions aleatory distributed to multiple responses. Milsa was used as an assessment in a gamified discipline [1]. After the automatic correction of the questions, the students evaluate the responses. The correction algorithm in Milsa is based on LSA [2], on STASIS [3] and on Michalski's genetic algoritm [4]. The template answer is a tagged text: hyperonymy, hyponymy, meronymy and synonymy are marked. By the end the tests on are showed and evaluated.

Technological advances in information and communications systems are enabling access to education for millions of students, regardless of their geographical location. In this sense, distance learning schools take advantage of technological tools, such as course management systems (CMS) and their plugins. This work proposes a plugin for the e-learning platform Moodle that automatically sends e-mail messages to the students in order to motivate them to take part in the distance learning courses and, consequently, to reduce the dropout rates. This plugin has been tested in courses promoted by the General Coordination of Distance Education (CGEAD) of the Brazilian National School of Public Administration (ENAP). Based on data from its operation within the real ENAP distance learning environment, the proposed plugin is particularly successful to tackle the enrollment locking cases that compose the student dropout rate and also to motivate course enrolled students to access the virtual learning system more frequently.

This paper aims to present a students' clustering method based on their backward knowledge of a specific domain. The categorization process uses the tracking information of students' answers in multiple choice questions (MCQ). We used an enhanced MCQ representation in which all the choice items (right or wrong) are linked to an existing conceptual map. We track the students' answers and propose a method to identify each student's profile on a certain subject and then cluster all the students' similar profiles. The resulting grouping map indicates how much the students learned about concepts on the knowledge domain.

Legacy system modernization has gained increasing attention from both researchers and practitioners, mainly due to the need of maintaining legacy systems towards business needs and technology advances.In this way, a set of techniques, tools and terms related to software modernization have been proposed-although they have not been consolidated yet.This hinders the characterization of real modernization scenarios according to the existing literature.This paper synthesize the existing contributions related to software modernization by means of a mapping study that characterizes the main results in terms of proposed processes, techniques, and tools.As one of our main findings, we report a lack of empirical studies trying to understand the benefits of using the existing approaches for software modernization

Service-oriented computing has emerged as an effective approach for integrating business (and systems) that might spread throughout different organizations. A service is a unit of logic modularization that hides implementation details using well-defined contracts. However, existing languages for contract specification in this domain present several limitations. For instance, both WSDL and Swagger use language-independent data formats (XML and JSON) that are not suitable for specifying contracts and often lead to heavyweight specifications. Interface description languages, such as CORBA IDL and Apache Thrift, solve this issue by providing specific languages for contract specifications. Nevertheless, these languages do not target to the REST architectural style and lack support for language extensibility. In this paper we present the design and implementation of NeoIDL, an extensible domain specific language and program generator for writing REST based contracts that are further translated into service’s implementations. In addition, we also present a systematic evaluation of our approach from different perspectives, which involved the implementation of different services using NeoIDL from the domain of Command & Control. In particular, we found initial evidences that shows that NeoIDL can contribute: (i) to bring return on investment with respect to the design and development of NeoIDL, after the implementation of 4 to 7 services; and (ii) to reduce significantly the number of lines of specification when compared to an existing service specification language such as Swagger.

To provide better life quality for mankind through the use of electrical and electronic devices that surround the world, controlling and communication between these equipments must occur and it must happen in an automatic and transparent way. This paper proposes a model based on usual technology to enable the event reading and the controlling of electrical devices, already existent and later developed, through a RESTful API or UPnP technology in order to allow the easy development of applications that can effectively improve life by transparent interaction with regular people. Furthermore, it will be proposed two direct applications of these APIs and it will be shown how to extend it.

Computing systems trust and reputation representation have been widely discussed and applied in a lot of information technology scenarios, becoming subject of scientific researches both from a theoretical and practical point of view. This paper proposes the development of a trust model to ensure a reliable files exchange among users in a private cloud, as well as the calculation process of trust among these users, according to the metrics previously established. The simulation result shows that this trust model is robust because it successfully access node reliable under Private Cloud Computing environment.