Variational Data Structures

Walkingshaw, Eric; Kästner, Christian; Erwig, Martin; Apel, Sven; Bodden, Eric

doi:10.1145/2661136.2661143

Cited by 34 publications

(4 citation statements)

References 54 publications

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“…Variational typing is defined in terms of the choice calculus (Erwig & Walkingshaw 2011). Other applications of the choice calculus include the development of variational data structures (Walkingshaw et al, 2014; Meng et al, 2017; Smeltzer & Erwig 2017) to support variational program execution (Erwig & Walkingshaw 2013; Nguyen et al, 2014; Chen et al, 2016), and view-based editing of variational programs (Walkingshaw & Ostermann 2014; Stănciulescu et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Migrating gradual types

Campora¹,

Chen²,

Erwig³

et al. 2022

J. Funct. Prog.

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Gradual typing allows programs to enjoy the benefits of both static typing and dynamic typing. While it is often desirable to migrate a program from more dynamically typed to more statically typed or vice versa, gradual typing itself does not provide a way to facilitate this migration. This places the burden on programmers who have to manually add or remove type annotations. Besides the general challenge of adding type annotations to dynamically typed code, there are subtle interactions between these annotations in gradually typed code that exacerbate the situation. For example, to migrate a program to be as static as possible, in general, all possible combinations of adding or removing type annotations from parameters must be tried out and compared. In this paper, we address this problem by developing migrational typing, which efficiently types all possible ways of replacing dynamic types with fully static types for a gradually typed program. The typing result supports automatically migrating a program to be as static as possible or introducing the least number of dynamic types necessary to remove a type error. The approach can be extended to support user-defined criteria about which annotations to modify. We have implemented migrational typing and evaluated it on large programs. The results show that migrational typing scales linearly with the size of the program and takes only 2–4 times longer than plain gradual typing.

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Section: Related Workmentioning

confidence: 99%

Migrating gradual types

Campora¹,

Chen²,

Erwig³

et al. 2022

J. Funct. Prog.

Self Cite

View full text Add to dashboard Cite

show abstract

“…ART on the other hand, can handle variations at any levels of granularity. Walkingshaw et al [35] provided a systematic and broader perspective on variational data structures. Properties related to program customizations are encapsulated in these variability-aware data structures.…”

Section: Comparison With Other Techniquesmentioning

confidence: 99%

Managing Big Clones to Ease Evolution: Linux Kernel Example

Kumar

Jarzabek

Dan³

2016

Annals of Computer Science and Information Systems

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Abstract-Successful software is often enhanced and adapted to the needs of new users. During evolution, a software system grows in size, becomes more complex, and costly to maintain. In this paper, we point to big clones-large granular duplicated program structures such as files or directories-as one of many reasons why this happens. Using the Linux kernel as an example, we show that big clones arise in the Linux kernel despite careful architecture design and a systematic approach for managing variability. We propose a solution to avoid these big clones by representing them as generalized templates in ART (Adaptive Reuse Technique). ART templates are constructed on top of the Linux code, without conflicts with the state-of-art techniques and tools used to manage the Linux kernel. Benefits include simplification of the Linux kernel due to non-redundancy, easier comprehension, and traceability of the change impact during evolution. The proposed technique is general and the Linux example discussed in this paper also illustrates general phenomena.

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“…To evaluate the expressions and thus check the reliability of the product line, we leverage ADDs as variational data structures [Walkingshaw et al, 2014]. The goal is to save processing time by storing reliability values for valid congurations in data structures that provide for ecient arithmetics.…”

Section: Family-based Phasementioning

confidence: 99%

“…While an expression is evaluated with real values, a lifted expression is evaluated using ADDs that represent Boolean functions from features to real values. Each of these ADDs encode the values that a variable can assume according to each possible conguration, also known as variational data [Walkingshaw et al, 2014]. Since this approach incorporates the knowledge of valid feature combinations, it is a family-based strategy.…”

Section: Family-based Strategymentioning

confidence: 99%

Estratégias comutativas para análise de confiabilidade em linha de produtos de software

Castro¹

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AgradecimentosÀ Isabela, minha amada esposa e amiga, sem a qual este trabalho não teria sido possível.Obrigado por compreender os momentos em que eu estive ausente e por me fazer parar de trabalhar, vez ou outra, para refrescar a cabeça.Aos meus pais, Valéria e Gilvan, e ao meu irmão, Miguel, pela conança que sempre depositaram em mim. Obrigado, especialmente, por me ensinarem a buscar respostas por mim mesmo e me motivarem, constantemente, a superar minhas próprias limitações.Ao meu orientador, Prof. Vander Alves, pela inestimável orientação ao longo do desenvolvimento deste trabalho e por me apresentar ao fantástico mundo das linhas de produtos. Obrigado por me estimular a explorar idéias interessantes, mesmo quando eu não via luz no m do túnel. Estendo os agradecimentos ao meu coorientador, Prof. Leopoldo Teixeira, pelas discussões sempre produtivas. Mais do que orientar, vocês participaram ativamente da pesquisa e me proporcionaram um aprendizado sem tamanho.Ao colega de orientação, André Lanna, por ter idealizado a ReAna e ter semeado a idéia de nossa linha de produtos de ferramentas de análise. Obrigado por compartilhar sua experiência acadêmica e suas dicas musicais.Aos professores Pierre-Yves Schobbens e Sven Apel, pelas revisões rigorosas e pelo feedback preciso. Suas contribuições para a consistência deste trabalho foram inestimáveis.Estendo os agradecimentos aos membros da banca examinadora, professores Alexandre Mota e Maurício Ayala-Rincón, pelas sugestões e críticas que contribuíram para o aprimoramento do trabalho.Ao Exército Brasileiro, pelas oportunidades ímpares de crescimento prossional. Agradeço, em particular: ao TC Alisson, amigo e mentor na engenharia de software; ao Cel Kohl, pela conança, pela paciência e por sempre advogar em prol da realização deste trabalho; ao Exmo. Sr. Gen Bráulio, por visualizar a importância do aperfeiçoamento acadêmico de seus subordinados e conar que eu me tornaria um bom mestre; ao Ricardo, que tentou durante muitos anos me convencer a prosseguir nos estudos acadêmicos (até que conseguiu); e a todos os demais amigos da Divisão de Comando e Controle, pela compreensão nos meus momentos de ausência e pela excelência prossionalvocês me inspiram a ser um engenheiro melhor a cada quantum de tempo arbitrariamente denido.

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Variational Data Structures

Cited by 34 publications

References 54 publications

Migrating gradual types

Migrating gradual types

Managing Big Clones to Ease Evolution: Linux Kernel Example

Estratégias comutativas para análise de confiabilidade em linha de produtos de software

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