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Plagiarism is one of the most serious academic offenses. However, people have adopted different approaches to avoid plagiarism, such as transcribing excerpts from one language. Thus, it is challenging to realize this plagiarism form unless someone fully understands another language. Researchers have developed approaches for detecting plagiarism in a variety of different languages. However, most methods created in the past have proved effective for detecting plagiarism in papers published in a single language, most notably English. Therefore, this paper aims to provide a systematic literature review of cross-language plagiarism detection methods (CLPD) in a natural language context. The approach used to perform this study consisted of an extensive search for relevant literature through an SLR and Snowballing. Therefore, we present an overview of (i) cross-language plagiarism detection techniques; (ii) the artifacts and the aspects that were considered in the evaluation phase; and (iii) the lack of guidelines and tools for its implementation. Its contribution lies in its ability to highlight emerging cross-language plagiarism detection techniques trends. Further, we identify any of these techniques in other domains, for instance, software engineering

Context: While most research shows positive effects of gamification, the focus on its adverse effects is considerably smaller. Objective: To help gamification designers make more informed decisions when selecting game design elements to be included in education/learning systems, raising awareness on potential negative effects. Method: We conducted a systematic mapping study of the negative effects of game design elements on education/learning systems. We also held a focus group discussion with developers of a gamified system, discussing the mapping study results with regard to their awareness and perceptions on the reported negative effects. Results: The mapping study revealed 87 papers reporting undesired effects of game design elements. We found that badges, leaderboards, competitions, and points are the game design elements most often reported as causing negative effects. The most cited negative effects were lack of effect, worsened performance, motivational issues, lack of understanding, and irrelevance. The ethical issues of gaming the system and cheating were also often reported. As part of our results, we map the relations between game design elements and the negative effects that they may cause. The focus group revealed that developers were not aware of many of the possible negative effects and that they consider this type of information useful. The discussion revealed their agreement on some of those potential negative effects and also some positive counterparts. Conclusions: Gamification, when properly applied, can have positive effects on education/learning systems. However, gamified systems are also prone to generate harmful effects. Revealing and discussing potentially negative effects can help to make more informed decisions considering their trade-off with respect to the expected benefits., This study was financed in part by the Coordination for the Improvement of Higher Education Personnel (CAPES - Coordena����o de Aperfei��oamento de Pessoal de N��vel Superior), Brazil, and by the National Council for Scientific and Technological Development (CNPq - Conselho Nacional de Desenvolvimento Cient��fico e Tecnol��gico), Brazil - Finance Code 001.

Public repository for Software Product Line Scoping: A Systematic Literature Review This work is part of a project developed within the Laboratory of Empirical Studies in Software Engineering (http://lesse.com.br/) Abstract: Software product line (SPL) scoping aids companies to define the boundaries of their products. As scoping guides the management of the resources in SPL development, it becomes one of the core activities in this process. One may find in the literature, several approaches on this topic, proposing techniques and methodologies to be applicable in different organizational scenarios. Due to the importance of such approaches, there is a need for an update in the state of the art in this field. In this context, we look into identifying, analyzing, and extracting important information from SPL scoping proposals found in the literature. This important information includes similarities and differences among these approaches as well as scoping concepts and research opportunities. Thus, we conducted a systematic literature review (SLR) alongside snowballing, following a well-defined protocol to retrieve, classify and extract information from the literature. We analyzed a total of 58 studies, identifying 41 different approaches in the field, highlighting their similarities and differences, and establishing a generic scoping process. Furthermore, research opportunities in the SPL scoping field are discussed. This SLR was conducted using the Thoth tool (http://lesse.com.br/tools/thoth/).

Image descriptions intend to express, in words, the visual content and are essential for people who do not have eyesight. Such descriptive sentences are generated manually or by Artificial Intelligence (AI) models. Despite its relevance, the emergence of automatic description generators was not motivated by people with visual impairments; thus, they still cause dissatisfaction in their audience. In this study, we investigate image descriptions issues reported in the literature through the Snowballing technique, where we found thirteen problems, including those related to Ethics, such as physical appearance, gender and identity, race, and disability. We have identified five reasons why sighted people do not write descriptions for visual content, raising the need for accessibility campaigns to make them aware of the social importance of image descriptions. In addition, we conducted a set of interviews with eight low vision participants. We explored the characteristics of the descriptive sentences of 25 indoor images and collected the participants? expectations of image descriptions. Therefore, through the results of the Snowballing and the interviews, we propose a set of Best Practices to help automatic tools and sighted people in writing more satisfactory and quality descriptive sentences. We hope our results will highlight the social relevance of image descriptions and encourage the community to pursue further interdisciplinary researches that could potentially minimize the issues encountered in our study. Descri??es de imagens visam expressar, em palavras, o conte?do visual e s?o essenciais para pessoas que n?o t?m vis?o. Tais senten?as descritivas s?o geradas manualmente ou por modelos de Intelig?ncia Artificial (IA). Apesar da sua relev?ncia, a emerg?ncia de geradores de descri??es autom?ticas n?o foi motivada por pessoas com defici?ncia visual. Portanto, elas ainda causam insatisfa??o em sua audi?ncia. Neste estudo, n?s investigamos problemas em descri??es de imagens na literatura por meio da t?cnica de Snowballing, onde encontramos treze problemas, incluindo aqueles relacionados ? ?tica, tais como a apar?ncia f?sica, g?nero e identidade, ra?a e defici?ncia. N?s identificamos cinco raz?es do porqu? pessoas videntes n?o escrevem descri??es para os conte?dos visuais, demonstrando a necessidade de campanhas de acessibilidade para conscientiz?-las da import?ncia social das descri??es de imagens. Al?m disso, n?s realizamos um conjunto de entrevistas com oito participantes com baixa vis?o. N?s exploramos as caracter?sticas das senten?as descritivas de 25 imagens de ambientes internos e coletamos as expectativas de descri??es de imagens dos participantes. Portanto, atrav?s dos resultados do Snowballing e das entrevistas, n?s propomos um conjunto de Boas Pr?ticas para auxiliar as ferramentas autom?ticas e as pessoas videntes na escrita de descri??es de imagens de mais satisfat?rias e de qualidade. N?s esperamos que os nossos resultados ressaltem a relev?ncia social de senten?as descritivas e encorajam a comunidade a prosseguir com pesquisas interdisciplinares que possam potencialmente minimizar os problemas encontrados no nosso estudo.

Public repository for Software Product Line Scoping: A Systematic Literature Review This work is part of a project developed within the Laboratory of Empirical Studies in Software Engineering (http://lesse.com.br/) Abstract: Software product line (SPL) scoping aids companies to define the boundaries of their products. As scoping guides the management of the resources in SPL development, it becomes one of the core activities in this process. One may find in the literature, several approaches on this topic, proposing techniques and methodologies to be applicable in different organizational scenarios. Due to the importance of such approaches, there is a need for an update in the state of the art in this field. In this context, we look into identifying, analyzing, and extracting important information from SPL scoping proposals found in the literature. This important information includes similarities and differences among these approaches as well as scoping concepts and research opportunities. Thus, we conducted a systematic literature review (SLR) alongside snowballing, following a well-defined protocol to retrieve, classify and extract information from the literature. We analyzed a total of 58 studies, identifying 41 different approaches in the field, highlighting their similarities and differences, and establishing a generic scoping process. Furthermore, research opportunities in the SPL scoping field are discussed. This SLR was conducted using the Thoth tool (http://lesse.com.br/tools/thoth/).

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE., Author version of manuscript accepted for publication at the Information and Software Technology Journal

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Background: In any area of science, all proposed technology should be evaluated and characterized so that the community can understand when, where and how to apply it. Particularly in the Software Engineering field, the concern with this characterization and the characterization of the technology is known as Evidence-Based Software Engineering (EBSE). Secondary studies contribute to this characterization because they use more rigorous research methods. Snowballing is a technique to identify relevant primary studies in secondary studies. It is well accepted by the community but may require a great deal of effort in its implementation depending on the guidelines adopted and on the number of existing relevant primary studies. Objective: To propose criteria for prioritizing primary studies for applying snowballing using the results obtained from search strings as seed aiming at improving the efficiency when applying the technique, providing computational support for its application. Method: Four criteria were proposed for prioritization of primary studies and algorithms were developed that allow computational support for applying the criteria. The evaluation was carried out by means of experimental studies, being an exploratory study and two experiments. Results: The best results were obtained by combining the four proposed criteria, resulting in a precision of 96.46% and a recall of 51.24%, with an average error rate of 20%. Algorithms created for reference extraction and similarity calculation presented satisfactory results. Conclusion: Based on the obtained results, there are indications that the proposed prioritization criteria, which make use of the reference extraction and similarity algorithms developed and implemented in the StArt tool, contribute to increase the efficiency of the snowballing technique without having a significant loss of quality of the results. Contexto: Em qualquer área da ciência, toda tecnologia proposta deveria ser avaliada e caracterizada para que a comunidade possa entender quando, onde e como aplicá-la. Particularmente, na área de Engenharia de Software, a preocupação constante com essa caracterização e a própria caracterização da tecnologia é referenciada como Engenharia de Software Baseada em Evidência (ESBE). Estudos secundários contribuem para construir essa caracterização porque utilizam métodos mais rigorosos de pesquisa. Snowballing é uma técnica de busca de estudos primários utilizada em estudos secundários. Ela é bem aceita pela comunidade, mas pode demandar um grande esforço em sua execução dependendo das diretrizes adotadas e do número de estudos primários relevantes existentes. Objetivo: Propor critérios de priorização de estudos primários para snowballing com conjunto inicial formado por buscas com strings para melhorar a eficiência na aplicação da técnica, fornecendo suporte computacional para sua aplicação. Método: Foram propostos quatro critérios de priorização de estudos primários e desenvolvidos algoritmos que permitem o suporte computacional à aplicação dos critérios. A avaliação foi realizada por meio de estudos experimentais, sendo um estudo exploratório e dois experimentos. Resultados: Os melhores resultados foram obtidos ao combinar os quatro critérios propostos, resultando em uma precisão de 96,46% e recall de 51,24%, tendo um percentual médio de erro de 20%. Os algoritmos criados para reconhecimento de referências e cálculo de similaridade apresentaram resultados satisfatórios. Conclusão: Com base nos resultados obtidos, há indícios de que os critérios de priorização propostos, que fazem uso dos algoritmos de extração de referências e de similaridade desenvolvidos e implementados na ferramenta StArt, contribuem para o aumento da eficiência na aplicação da técnica snowballing sem que haja perda significativa de qualidade dos resultados. CAPES: Código de Financiamento 001

Context: The amount of software in solutions provided in various domains is continuously growing. These solutions are a mix of hardware and software solutions, often referred to as software-intensive systems. Companies seek to improve the software development process to avoid delays or cost overruns related to the software development. Objective: The overall goal of this thesis is to improve the software development/building process to provide timely, high quality and cost efficient solutions. The objective is to select the origin of the components (in-house, outsource, components off-the-shelf (COTS) or open source software (OSS)) that facilitates the improvement. The system can be built of components from one origin or a combination of two or more (or even all) origins. Selecting a proper origin for a component is important to get the most out of a component and to optimize the development. Method: It is necessary to investigate the component origins to make decisions to select among different origins. We conducted a case study to explore the existing challenges in software development. The next step was to identify factors that influence the choice to select among different component origins through a systematic literature review using a snowballing (SB) strategy and a database (DB) search. Furthermore, a Bayesian synthesis process is proposed to integrate the evidence from literature into practice. Results: The results of this thesis indicate that the context of software-intensive systems such as domain regulations hinder the software development improvement. In addition to in-house development, alternative component origins (outsourcing, COTS, and OSS) are being used for software development. Several factors such as time, cost and license implications influence the selection of component origins. Solutions have been proposed to support the decision-making. However, these solutions consider only a subset of factors identified in the literature. Conclusions: Each component origin has some advantages and disadvantages. Depending on the scenario, one component origin is more suitable than the others. It is important to investigate the different scenarios and suitability of the component origins, which is recognized as future work of this thesis. In addition, the future work is aimed at providing models to support the decision-making process.

Context: Despite continuously increasing importance of Agile and Lean in software development, the number of studies that investigate on use of metrics relevant to Agile or Lean are limited and yet few studies implements was unclear. Unclear is which are the prominent metrics that are useful in industries, and their purpose of usage. Objectives: Main goal of this study is to find the metrics useful in Agile and Lean practicing industries; that are evaluated in industries by systematically identifying all the metrics from empirical evidence found in Literature as well as verifying which of them are prominently being used in industries. In addition, the purpose of using these metrics in industries are reported, and causes for dissatisfaction on use of some of the identified metrics among surveyed companies are investigated and reported. Methods: Two research methodologies are used; Systematic Literature Review (SLR) and Industrial Survey. SLR is performed using snowballing as search approach to select primary studies. SLR is used to identify all the metrics that are useful for Agile and Lean software development. Rigor and relevance analysis is performed to assess the quality of the resulted primary studies. Industrial survey was conducted in order to verify and extend the empirical evidence exists in Literature regarding metrics by finding which of them are more prominently being used. Moreover causes for dissatisfaction over outcome of metrics use for process improvements were observed by performing comparative analysis between unsatisfied respondents results and satisfied respondents results. Results: In total 20 metrics were identified from the studies having high rigor and high relevance. Moreover 11 out of these 20 metrics were identified to be prominently being used in industries using survey and other 9 metrics are found useful for Agile or Lean methods but need more awareness. Evidence from both SLR and survey shows that most of these identified or potential metrics are used for time associated purposes which are predictability, tracking, forecasting or planning, and very little evidence found for metrics that are being used directly for quality purpose. It was observed that some of the surveyed respondents who answered not satisfied with the metrics being used are not aware of the potential benefits these metrics can offer in Agile or Lean settings. Conclusion: Evidence from both SLR and survey shows that the identified 20 metrics are very important and useful for Agile or Lean methods. 11 out of these 20 metrics are prominently being used by industries and evidence shows for other 9 metrics are also useful for Agile but needs more awareness for industries to realize their potential benefits in large scale. Also, more evidence is found for metrics that are used for time related purposes which are being dominant and important in industries than quality focused metrics. Therefore, it is important for industries not only to know which metrics are appropriate for Agile or Lean but also to have a deep understating of metrics behaviors. This will help to realize the level predictability these metric’s offer in order to make right assumptions or planning. Mobile no: (+46)-723110118

Systematic studies of the literature can be done in different ways. In particular, different guidelines propose different first steps in their recommendations, e.g. start with search strings in different databases or start with the reference lists of a starting set of papers. In software engineering, the main recommended first step is using search strings in a number of databases, while in information systems, snowballing has been recommended as the first step. This paper compares the two different search approaches for conducting literature review studies. The comparison is conducted by searching for articles addressing "Agile practices in global software engineering". The focus of the paper is on evaluating the two different search approaches. Despite the differences in the included papers, the conclusions and the patterns found in both studies are quite similar. The strengths and weaknesses of each first step are discussed separately and in comparison with each other. It is concluded that none of the first steps is outperforming the other, and the choice of guideline to follow, and hence the first step, may be context-specific, i.e. depending on the area of study.

This paper provides a methodological assessment of the advantages and drawbacks of the origin-based snowballing technique as a reliable method to construct representative samples of international migrants in destination areas. Using data from the MAFE-Senegal Project, our results indicate that this is a very risky method in terms of quantitative success. Besides, it implies some clear selection biases: it overrepresents migrants more strongly connected to their home country, and it tends to overestimate both poverty in households at origin and the influence of previous migration experiences of social networks on individuals’ out-migration.

Beyond the initial phases of systems design Participatory Design has potentiality to include operation and maintenance of IT systems in organizations. The paper presents this argument through reports from case studies of local IT-support, here coined 'participatory IT-support'. The paper presents characteristics of participatory ITsupport and suggests a method for identifying qualified candidates for the support position in organizations.