JSON-to-JSON transformation languages enable the transformation of a JSON document into another JSON document. As JSON is gradually becoming the most used interchange format on the Internet there is a need for transformation languages that can transform the data stored in JSON in order for the data to be used with other systems. The transformation can transform the document structurally, for example by altering the hierarchical structure of the document. The transformation can also transform the document textually, for example by renaming fields or altering values. None of the existing JSON-to-JSON transformation languages have become a standard (Jellife, 2017). This work evaluates the expressive power of the JSON-to-JSON transformation language Jolt. Jolt have recently been adopted by Apache and support have been introduced in some of their products. If a transformation language have expressive power that are at least equal to Nested Relational Algebra this implies that a transformation language can perform many advanced transformations. In this work  a formal model of Jolt is defined, referred to as Jolt0, in order to compare its expressive powers to Nested Relational Algebra. For that purpose, the operations of another formal model called MQuery which have been proven to have equivalent expressive power to Nested Relational Algebra are translated into Jolt0. It is shown that Jolt does not have expressive powers equivalent to Nested Relational Algebra. We further compared the performance of four JSON-to-JSON transformation languages (Jolt, Handlebars, Liquid, and XSLT 3.0) by constructing tests where the different transformation languages executed equivalent transformations. The transformations were evaluated by measuring runtime and memory usage. The study shows that XSLT 3.0 performed worst in all run time and memory usage tests. When transforming large input data XSLT 3.0 performed significantly worse than the other languages. / JSON-till-JSON transformationsspråk möjliggör transformationer från ett JSON-dokument till ett annat JSON-dokument. Eftersom JSON gradvis håller på att bli det mest använda data-utväxlingsformatet på internet så finns det ett behov av transformationsspråk som kan transformera data som är lagrad i JSON formatet för att kunna användas med andra system. Transformationen kan transformera dokumentet strukturellt, till exempel genom att förändra den hierarkiska strukturen på dokumentet. Transformationen kan även transformera dokumentet textuellt, till exempel genom att döpa om fält eller ändra värden. Ingen av de existerande JSON-till-JSON transformationsspråken har blivit en standard (Jellife, 2017). Det här arbetet undersöker uttryckskraften av Jolt vilket är ett JSON-till-JSON transformationsspråk. Jolt har nyligen fått stöd av Apache i några av deras produkter. Om ett transformationsspråk har en uttryckskraft som är ekvivalent med nästlad relationell algebra innebär det att språket kan utföra många avancerade transformationer. I det här arbetet definieras en formell modell av Jolt, kallad Jolt0, för att kunna jämföra dess uttryckskraft med nästlad relationell algebra. Till det syftet så översätts operationerna från en annan formell modell med namnet MQuery som har bevisats ha ekvivalent uttrykskraft med nästlad relationell algebra till Jolt0. Arbetet drar slutsatsen att Jolt inte har uttryckskraft som är ekvivalent med nästlad relationell algebra. Arbetet undersöker också prestandan för de fyra JSON-till-JSON transformationsspråken (Jolt, Handlebars, Liquid och XSLT 3.0) genom att konstruera tester där de olika transformationsspråken exekverar ekvivalenta transformationer. Transformationerna utvärderas baserat på körstids- och minnesanvändningsprestandan. Studien visar att XSLT 3.0 presterar sämst i alla körstids- och minnesanvändningstester. När transformationerna använder sig av stor input data så presterar XSLT 3.0 signifikant sämre än de andra språken.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2017-09-13T22:00:52Z No. of bitstreams: 1 Tese Uma abordagem para análise de impacto de mudança ... Andreza de Sousa Vieira.pdf: 1136053 bytes, checksum: bea3c2c4cd1e05c961abf7ec7c127557 (MD5) / Made available in DSpace on 2017-09-13T22:00:53Z (GMT). No. of bitstreams: 1 Tese Uma abordagem para análise de impacto de mudança ... Andreza de Sousa Vieira.pdf: 1136053 bytes, checksum: bea3c2c4cd1e05c961abf7ec7c127557 (MD5) Previous issue date: 2014-12-17 / Capes / MDD (Model-Driven Development) tem como principal objetivo deslocar o foco da implementação do código fonte para o desenvolvimento de modelos dentro do processo de desenvolvimento de software. Elementos que desempenham um papel muito importante dentro de MDD são as transformações de modelos, isto é, regras que descrevem como modelos de Origem devem ser usados para gerar automaticamente um ou mais modelos de destino. Como qualquer projeto de software, os projetos baseados em MDD evoluem ao longo do ciclo de vida, tendo em vista que mudanças em suas transformações são frequentes. Antes de aplicar qualquer mudança em uma transformação é importante analisar como ela pode afetar o projeto como um todo. No entanto, atualmente não há nenhuma técnica para auxiliar os gerentes de projeto ou desenvolvedores nesse sentido. O objetivo deste trabalho é propor uma abordagem para análise do impacto de mudanças em transformações de modelos. Baseada na análise estática,a abordagem proposta visa: (i)identificar o conjunto de elementos impactados por uma mudança na transformação; e (ii) mensurar o impacto da mudança através de um conjunto de métricas. Dentre outros benefícios, a abordagem proposta pode auxiliar: (i) os gerentes de projeto a melhor escalonar e priorizar mudanças, uma vez que eles podem estimar os custos da aplicação de cada uma delas; e (ii) os desenvolvedores a identificar, automaticamente, todos os elementos impactados com uma mudança, assim, economizando esforço e tempo de desenvolvimento. Um estudo de caso foi conduzido para identificar novas métricas e para avaliar a abordagem proposta em relação à análise manual do impacto de mudanças em diversas transformações reais. Com o estudo, foi possível perceber que os resultados obtidos pela abordagem proposta e pela análise manual foram bastante próximos. Porém, considerando o esforço e o tempo de análise, os resultados da abordagem proposta foram melhores. / The main goal of the MDD (Model-Driven Development) approach is to change the focus from source code to models within the software development process. Elements that play important role in MDD are model transformations, i.e. rules that describe how source models should be used to automatically generate one or more target models. As any software project, MDD-based projects evolve along their lifecycle in such way that changes in their transformations are frequent. Before applying any change to a transformation, it is important to analyze how it would impact the whole project. However, there is currently no technique to help project managers or developers in this direction. The objective of this work is to propose an approach to analyze the impact of changes resulting from model transformations. Based on static analysis, the proposed approach aims at: (i)identifying the set of elements impacted with a change applied to a transformation; and (ii)measuring the impact of a change through a set of metrics. Among other benefits ,the proposed approach can help: (i)project managers to better prioritize and schedule changes, since they can estimate the costs to apply each of them; and (ii)developers to automatically identify all elements impacted with a change, thus saving effort and development time. A case study was conducted to identify new metrics and to evaluate the proposed approach regarding to the manual change impact analysis considering real transformations. This study revealed that the results obtained by using the proposed aproach and using the manual analysis were very similar. However, regarding effort and time, we observed that the results obtained from the proposed approach were superior than the manual analysis.