This thesis covers the systematic mapping of established public Application Programming Interface (API)s that are employing the Artificial Intelligence (AI) technology. This due to the fact that the problem has been the lack of systematic maps of AI APIs in the present time, therefore this thesis has the purpose of increasing the insight in the area by creating the mapping study. The goal is to provide both a basis for research and to aid the general developer which uses the AI APIs. The systematic mapping of the AI APIs will be conducted by examining the information of the APIs and iterations classifying the AI APIs into categories and presented in tables. The analysis and discussion of the study was made based on the results from the study, namely the phases, the iterations, the result tables and the final systematic map. Additionally, an analysis was made on the validity threats of the study. The evaluation of each API in this study was done in cycles, by categorizing each AI API into a category that is included in the final result, which is a systematic map. The result has been proven to be useful for the target group of this study, the researchers and developers, by aiding them in finding the right API for them to use in their work. Therefore, this work will help future developers and researchers due to the fact that the thesis is providing relevant information in the development phases of employing AI in the web interfaces at the present time. / Detta examensarbete omfattar en systematisk kartläggning av etablerade publika API:er som använder sig utav AI- teknik. Eftersom bristen på systematiska kartor över AI API:er varit stor, har detta examensarbete syfte att öka insikten i området genom att skapa denna kartläggningsstudie. Målet med den systematiska kartläggningen är att bygga en grund för framtida forskning inom detta område, samt förenkla processen av att hitta AI API:erna för den allmänna utvecklaren som använder dessa. Den systematiska kartläggningen av AI API:er kommer att genomföras genom att undersöka tillgänglig dokumentation och information om API:erna. Dessutom klassificerades AI API:erna i kategorier, och presenterades sedan i tabeller. Analysen och diskussionen av studien gjordes baserat på resultaten från studien, nämligen faserna och iterationerna där AI API:erna kategoriseras, samt på resultat tabellerna och den slutliga systematiska kartan på AI API:erna. Dessutom gjordes en analys av hoten mot studiens giltighet. Utvärderingen av varje API i denna studie gjordes i cykler genom att kategorisera varje AI API i en kategori som sedan inkluderades i resultatet vilket är en systematiska karta. Resultatet från detta examensarbete har visat sig vara användbar för målgruppen, forskare och utvecklare, då det hjälper målgruppen att hitta rätt API att använda i arbetet.

Context and background: Requirements engineering(RE) is a first and a very important phase in any software development which helps in building a suitable and customer satisfactory product. In the past few years, the use of Agile software development has become popular in the industry. Customer communication plays an important role in any software development life cycle. Customers state the requirements needed to develop a product in the Requirements Engineering phase. A project is likely to fail due to problems in customer communication during the RE phase. Objective: This thesis aims to study the Customer communication challenges in Agile requirements engineering, prioritize these challenges, and also find out the mitigation strategies to overcome these challenges. Research Method: A systematic mapping study is conducted to find out the customer communication challenges. Based on the data collected from the systematic mapping study, a survey is conducted to find out the mitigation strategies to overcome the customer communication challenges faced in the RE phase and also prioritize these challenges. Results: Based on the data collected from the systematic mapping study, a total of 18 customer communication challenges are identified. In the second step, a survey is conducted based on the identified challenges. The prioritization of these challenges is done by calculating the risk analysis of the challenges from the survey data. And finally, mitigation strategies are mentioned to overcome all the identified 18 challenges.

In 2016, the Japanese Council for Science, Technology and Innovation (CSTI) proposed a project about sustainable smart societies known as Society 5.0. Society 5.0 advocates the use of advances in computer science and software engineering such as internet of things, artificial intelligence, augmented and virtual reality, as a way of resolving societal issues such as reducing the number of deaths in everyday traffic, reducing the amount of greenhouse gas emissions, and so forth. In this thesis, we report on the planning, execution, and results of a systematic mapping study aiming at understanding the role of software engineering in Society 5.0. To this end, we focus on identifying the main software engineering technologies enabling Society 5.0 as well as the main software engineering open challenges hampering its realisation. Starting from an initial set of 829 peer-reviewed publications and by means of a rigorous selection process, we selected a final set of 58 primary studies, which we analysed following a meticulous data extraction, analysis, and synthesis process. The selected primary studies described a set of 13 software engineering technologies acting as enablers of Society 5.0. The application of these technologies comes with technical challenges. In this respect, the selected primary studies identified 9 software engineering open challenges hampering the realisation of Society 5.0.

Systematic review of research manuscripts is a common procedure in which research studies pertaining a particular field or domain are classified and structured in a methodological way. This process involves, between other steps, an extensive review and consolidation of scientific metrics and attributes of the manuscripts, such as citations, type or venue of publication. The extraction and mapping of relevant publication data, evidently, is a very laborious task if performed manually. Automation of such systematic mapping steps intend to reduce the human effort required and therefore can potentially reduce the time required for this process.The objective of this thesis is to automate the data extraction and mapping steps when systematically reviewing studies. The manual process is replaced by novel graph modelling techniques for effective knowledge representation, as well as novel machine learning techniques that aim to learn these representations. This eventually automates this process by characterising the publications on the basis of certain sub-properties and qualities that give the reviewer a quick high-level overview of each research study. The final model is a concept learner that predicts these sub-properties which in addition addresses the inherent concept-drift of novel manuscripts over time. Different models were developed and explored in this research study for the development of concept learner.Results show that: (1) Graph reasoning techniques which leverage the expressive power in modern graph databases are very effective in capturing the extracted knowledge in a so-called knowledge graph, which allows us to form concepts that can be learned using standard machine learning techniques like logistic regression, decision trees and neural networks etc. (2) Neural network models and ensemble models outperformed other standard machine learning techniques like logistic regression and decision trees based on the evaluation metrics. (3) The concept learner is able to detect and avoid concept drift by retraining the model. / Systematisk granskning av forskningsmanuskript är en vanlig procedur där forskningsstudier inom ett visst område klassificeras och struktureras på ett metodologiskt sätt. Denna process innefattar en omfattande granskning och sammanförande av vetenskapliga mätvärden och attribut för manuskriptet, såsom citat, typ av manuskript eller publiceringsplats. Framställning och kartläggning av relevant publikationsdata är uppenbarligen en mycket mödosam uppgift om den utförs manuellt. Avsikten med automatiseringen av processen för denna typ av systematisk kartläggning är att minska den mänskliga ansträngningen, och den tid som krävs kan på så sätt minskas. Syftet med denna avhandling är att automatisera datautvinning och stegen för kartläggning vid systematisk granskning av studier. Den manuella processen ersätts av avancerade grafmodelleringstekniker för effektiv kunskapsrepresentation, liksom avancerade maskininlärningstekniker som syftar till att lära maskinen dessa representationer. Detta automatiserar så småningom denna process genom att karakterisera publikationerna beserat på vissa subjektiva egenskaper och kvaliter som ger granskaren en snabb god översikt över varje forskningsstudie. Den slutliga modellen är ett inlärningskoncept som förutsäger dessa subjektiva egenskaper och dessutom behandlar den inneboende konceptuella driften i manuskriptet över tiden. Olika modeller utvecklades och undersöktes i denna forskningsstudie för utvecklingen av inlärningskonceptet. Resultaten visar att: (1) Diagrammatiskt resonerande som uttnytjar moderna grafdatabaser är mycket effektiva för att fånga den framställda kunskapen i en så kallad kunskapsgraf, och gör det möjligt att vidareutveckla koncept som kan läras med hjälp av standard tekniker för maskininlärning. (2) Neurala nätverksmodeller och ensemblemodeller överträffade andra standard maskininlärningstekniker baserat på utvärderingsvärdena. (3) Inlärningskonceptet kan detektera och undvika konceptuell drift baserat på F1-poäng och omlärning av algoritmen.

Context. Process improvement using lean software development uses measures to answer information needs. Measures are important in process improvement as they identify whether improvements have been made or further improvements are needed. Objectives. This study aims to identify the measures proposed in literature for lean software development and structure them according to ISO/IEC 15939. Methods. The research methodology consists of systematic mapping and uses thematic analysis. Results. Lean software development literature has become more frequently published from 1996 to 2013. The most common research types were evaluation research and experience reports. Themes were identified in measures resulting in the identification of 22 base measures, 13 derived measures and 14 indicators in total. Identified measures were then structured using a proposed meta-model adapted from ISO/IEC 15939. Using the proposed meta-model twelve models of measures were instantiated. Conclusions. Gaps exist in the lean principles for deferring commitment and respecting people. Structuring measures in models presents opportunities to identify shared dependencies in measures. Lean software development research guidelines were defined. Further research into the comparison of indicators, the industrial use of measures and the representation of models of measures is needed.

In 2015, the United Nations (UN) put forward 17 Sustainable Development Goals (SDGs) to beachieved by 2030. Each member state should spare no effort to fulfill the goals for better lives. Thefirst objective of this study was to explore how Big Data could be used to monitor the progress,including the opportunities and challenges that such novel technologies brought. Previous studieswere reviewed critically for the first objective. The second objective was to find out what datainfrastructures were helpful in monitoring the achievement of SDG 7. A systematic mapping studywas performed to accomplish the second objective. 53 SDG-related academic papers were obtained.Their research data and where they were sourced from were manually analyzed and categorized togenerate data infrastructures for SDG 7. Besides, the automated qualitative coding was conductedbased on the manual structure to verify the manually identified data infrastructures and comparetheir frequencies in the selected papers. The methodology of combining manual and automatedqualitative analysis proposed in this study helped find a list of SDG 7 related data infrastructures.Although there were differences between the manual and automated results, the World Bank, UNdatabases, Eurostat, and IEA were considered the most frequently referred data sources; electricitydata and satellite imagery were regarded as the most commonly used data types.

[pt] A refatoração de código é uma transformação de código que visa aprimorar a estrutura interna do código. Uma refatoração isolada raramente é suficiente para remover completamente uma estrutura de código ruim, como uma anomalia de código. Os desenvolvedores então aplicam refatorações compostas para remover totalmente uma anomalia de código. Uma refatoração composta consiste em duas ou mais refatorações inter-relacionadas. Um refatoração composta é considerada completa quando elimina totalmente a anomalia de código alvo. Estudos relatam que os desenvolvedores geralmente falham em remover completamente as anomalias de código alvo por meio de refatorações compostas. Refatorações compostas concluídas podem não ser totalmente benéficas para a estrutura do código. Pois, estas podem induzir efeitos colaterais, como a introdução de anomalias de código ou a propagação de anomalias existentes. Há uma compreensão limitada sobre a completude das refatorações compostas e seus possíveis efeitos colaterais. Esta tese investiga como as refatorações compostas removem totalmente as anomalias de código sem induzir efeitos colaterais. Descobrimos que 64 por cento das refatorações compostas completas são formadas por tipos de refatoração não recomendados anteriormente. Dessa forma, derivamos um catálogo de recomendações para apoiar os desenvolvedores na aplicação de refatorações compostas. Na avaliação do catálogo, 85 por cento de 21 desenvolvedores relataram que usariam as recomendações do catálogo e que suas próprias soluções de refatoração teriam induzido efeitos colaterais. Também avaliamos qualitativamente três abordagens existentes para recomendar automaticamente refatorações compostas. Nesse estudo, a maioria (80 por cento) dos 10 desenvolvedores relatou que as abordagens existentes frequentemente induzem efeitos colaterais. No geral, as descobertas e o catálogo proposto podem ajudar os desenvolvedores a realizar refatorações compostas completas. / [en] Code refactoring is a code transformation that aims to enhance the internal code structure. A single refactoring is rarely sufficient to achieve the full removal of a poor code structure, such as a code smell. Developers then apply composite refactorings to fully remove a code smell. A composite refactoring (or, simply, composite) consists of two or more interrelated single refactorings. A composite is considered complete when it fully eliminates the target smell. However, studies report that developers often fail in completely removing target code smells through composites. Even when composite refactorings are complete they may still not be entirely beneficial to the code structure. They may induce side effects, such as the introduction of new smells or the propagation of existing ones. There is a limited understanding of the completeness of composite refactorings and their possible effects on structural quality. This thesis investigates whether and how composite refactorings fully remove smells without inducing side effects. We found that 64 per cent of complete composites in several software projects are formed of refactoring types not previously recommended in the literature. Based on this study, we derived a catalog of recommendations for supporting developers in applying composite refactorings. Out of twenty one developers evaluating our catalog, 85 per cent reported that they would use the catalog recommendations and that their own refactoring solutions would have induced side effects. We also qualitatively evaluated three existing approaches to automatically recommend composite refactorings. In our study with ten developers, most (80 per cent) developers reported that existing approaches frequently induce side effects. Overall, the findings and the proposed catalog can help developers to perform complete composite refactorings with better awareness of possible side effects.

[pt] Enquanto a maioria da pesquisa relata efeitos positivos de gamificação, o foco em seus efeitos adversos é consideravelmente menor. Tendo isso em mente, conduzimos um mapeamento sistemático dos efeitos negativos de elementos de game design em sistemas de aprendizado/educação. O estudo revelou 77 papers reportando efeitos não desejados de game design. Descobrimos que badges, competição, leaderboards e pontos são os elementos de game design mais encontrados sendo declarados como causadores de efeitos adversos. Os efeitos mais citados foram falta de efeito, falta de entendimento, irrelevância, problemas motivacionais e performance piorada. Então usamos os dados recolhidos para criar 7 diretrizes sobre alguns dos efeitos negativos encontrados e como lidar com eles. A dissertação pode ajudar designers de gamificação a tomarem decisões mais bem informadas quando selecionando elementos de game design a serem encontrados em sistemas de educação/aprendizado, criando percepção de efeitos adversos. / [en] While most research shows positive effects of gamification, the focus on its adverse effects is considerably smaller. Having this in mind, we conducted a systematic mapping study of the negative effects of game design elements on education/learning systems. The study revealed 77 papers reporting undesired effects of game design elements. We found that badges, competitions, leaderboards, and points are the game design elements most often reported as causing negative effects. The most cited negative effects were lack of effect, lack of understanding, irrelevance, motivational issues, and worsened performance. The ethical issue of cheating was also often reported. Then we used the data gathered to create 7 guidelines about some of the negative effects found and how to deal with those. This dissertation can 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.

The global threats of widespread biodiversity loss and climate change impact ecosystems daily, leading to far-reaching consequences for people who benefit from these systems in various ways. Despite these challenges, little is known about the differences in how people derive benefits from nature and who will become increasingly vulnerable to future global environmental change. Previous research has assessed the impact of inequality on nature’s benefits (or ecosystem services), but these efforts have primarily relied on socio-economic inequality measures. These measures overlook potential heterogeneity within groups based on characteristics such as gender, class, and ethnicity. Therefore, a multidimensional approach to understanding inequality is needed. This systematic mapping review aims to map the literature that explores multidimensional inequality in relation to ecosystem services (ES), specifically in terms of three types of ecosystem benefits: experience, use, and money. Key findings were that political-and economic inequalities are most frequently linked to all three types of benefits, with links between political inequality and the use of ES being particularly common in the literature. In contrast, studies that analyze inequalities based on religion, disabilities, and race in relation to ES are least common. Finally, experience is the least explored benefit in relation to inequality, potentially due to the challenges of assessing intangible benefits. This review also highlights the complexities of inequalities acting at different scales and potential trade-offs between inequality categories and ecosystem benefit types. Future research recommendations include further widening the inequality lens (primarily in the cultural and social dimensions) to improve our understanding of who benefits from nature to develop more effective, sustainable, and equitable policies and mitigate future threats.

[pt] Muitas startups e até mesmo empresas de software tradicionais adotaram o uso de MVPs (sigla em inglês para Produto Mínimo Viável) para permitir experimentar rapidamente possibilidades de solução. O conceito de MVP tem influenciado a forma como as equipes de desenvolvimento aplicam as práticas de Engenharia de Software (ES). Entretanto, o entendimento geral desta influência dos MVPs sobre as práticas de ES ainda é pobre. Nosso objetivo é caracterizar o panorama de publicações sobre práticas que têm sido utilizadas no contexto dos MVPs de software e reunir insights dos profissionais sobre as práticas identificadas. Conduzimos um estudo de mapeamento sistemático usando uma estratégia de busca híbrida que consiste em uma busca em banco de dados e um snowballing das referências, para frente e para trás, de forma paralela. Posteriormente, discutimos os resultados do mapeamento em duas sessões de grupos de foco envolvendo doze profissionais da indústria que utilizam amplamente os MVPs em seus projetos para capturar suas percepções sobre os resultados do mapeamento. Identificamos 33 artigos publicados entre 2013 e 2020. Observamos algumas tendências relacionadas às práticas de ideação e avaliação de MVPs. Por exemplo, com relação à ideação, encontramos seis abordagens diferentes (e.g., Design Thinking, Lean Inception) e principalmente práticas informais de envolvimento do usuário final (e.g., workshops, entrevistas). Com relação à avaliação, há uma ênfase nas validações do usuário final baseadas em práticas como testes de usabilidade, testes A/B, e análise dos dados de uso. Entretanto, ainda há pesquisas limitadas relacionadas à avaliação de viabilidade técnica do MVP e estimativa de esforço. Os praticantes das sessões do grupo de foco reforçaram a confiança em nossos resultados no que diz respeito às práticas de ideação e avaliação, estando cientes da maioria das práticas identificadas. Eles também relataram como lidam com as avaliações de viabilidade técnica (envolvendo desenvolvedores durante a ideação e conduzindo experimentos informais) e estimativa de esforço na prática (com base na opinião de especialistas e usando práticas comuns a metodologias ágeis, como o Planning Poker). Nossa análise sugere que há oportunidades para propostas de soluções e estudos de avaliação para tratar de lacunas na literatura relativas à avaliação de viabilidade técnica e estimativa de esforço. Em geral, é necessário investir mais esforço na avaliação empírica das práticas existentes relacionadas ao MVP. / [en] Many startup environments and even traditional software companies have embraced the use of MVPs (Minimum Viable Products) to allow quickly experimenting solution options. The MVP concept has influenced the way in which development teams apply Software Engineering (SE) practices. However, the overall understanding of this influence of MVPs on SE practices is still poor. Our goal is to characterize the publication landscape on practices that have been used in the context of software MVPs and to gather practitioner insights on the identified practices. We conducted a systematic mapping study using a hybrid search strategy that consists of a database search and parallel forward and backward snowballing. Thereafter, we discussed the mapping study results in two focus groups sessions involving twelve industry practitioners that extensively use MVPs in their projects to capture their perceptions on the findings of the mapping study. We identified 33 papers published between 2013 and 2020. We observed some trends related to MVP ideation and evaluation practices. For instance, regarding ideation, we found six different approaches (e.g., Design Thinking, Lean Inception) and mainly informal end-user involvement practices (e.g., workshops, interviews). Regarding evaluation, there is an emphasis on enduser validations based on practices such as usability tests, A/B testing, and usage data analysis. However, there is still limited research related to MVP technical feasibility assessment and effort estimation. Practitioners of the focus group sessions reinforced the confidence in our results regarding ideation and evaluation practices, being aware of most of the identified practices. They also reported how they deal with the technical feasibility assessments (involving developers during the ideation and conducting informal experiments) and effort estimation in practice (based on expert opinion and using practices common to agile methodologies, such as Planning Poker). Our analysis suggests that there are opportunities for solution proposals and evaluation studies to address literature gaps concerning technical feasibility assessment and effort estimation. Overall, more effort needs to be invested into empirically evaluating the existing MVP-related practices.