Projecting a Modular Future

Voelter, Markus; Warmer, Jos; Kolb, Bernd

doi:10.1109/ms.2014.103

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…First, considering that full feature modularity is almost impossible to achieve, and also does not appear to be necessary according to our study, we suggest considering lightweight modularity techniques. An interesting technique could be module projections [61][62][63], where feature code is physically scattered, but developers can easily obtain projections of modules in their IDE. Second, feature scattering should already be considered when designing the system architecture, accounting for some extent of scattering and possibly a limited number of highly scattered outliers.…”

Section: Resultsmentioning

confidence: 99%

A Study of Feature Scattering in the Linux Kernel

Passos¹,

Queiroz²,

Mukelabai³

et al. 2021

IIEEE Trans. Software Eng.

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Feature code is often scattered across a software system. Scattering is not necessarily bad if used with care, as witnessed by systems with highly scattered features that evolved successfully. Feature scattering, often realized with a pre-processor, circumvents limitations of programming languages and software architectures. Unfortunately, little is known about the principles governing scattering in large and long-living software systems. We present a longitudinal study of feature scattering in the Linux kernel, complemented by a survey with 74, and interviews with nine Linux kernel developers. We analyzed almost eight years of the kernel's history, focusing on its largest subsystem: device drivers. We learned that the ratio of scattered features remained nearly constant and that most features were introduced without scattering. Yet, scattering easily crosses subsystem boundaries, and highly scattered outliers exist. Scattering often addresses a performance-maintenance tradeoff (alleviating complicated APIs), hardware design limitations, and avoids code duplication. While developers do not consciously enforce scattering limits, they actually improve the system design and refactor code, thereby mitigating pre-processor idiosyncrasies or reducing its use. !

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Section: Resultsmentioning

confidence: 99%

A Study of Feature Scattering in the Linux Kernel

Passos¹,

Queiroz²,

Mukelabai³

et al. 2021

IIEEE Trans. Software Eng.

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“…MPS [53] provided capabilities to define a DSL trough many aspects: abstract aspects (metamodel), sematic aspects (constraints), concrete syntaxes aspects (graphical editors), generators aspects (model transformations) and many others (e.g. behavior, type system, data flow or intentions) [54]. MPS furnish two ways to reuse DSLs: the reference and the extension mechanisms.…”

Section: In Lopmentioning

confidence: 99%

A Practical Approach for Extending DSMLs by Composing their Metamodels

Abouzahra¹,

Sabraoui²,

Afdel³

2018

Adv. sci. technol. eng. syst. j.

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Domain specific modeling (DSM) has become popular in the software development field during these last years. It allows to design an application using a domain specific modeling language (DSML) and to generate an end-solution software product directly from models. However providing a new DSML is a complex and costly job. This can be reduced by the reuse of existing DSMLs to compose new ones trough a metamodel composition approach. This paper provides a composition rules based code generator facility for extending DSMLs. In doing so, it proposes three rules to compose DSMLs by composing there metamodels: reference rule, specialization rule and fusion rule. The results of an exploratory case study on using these rules are depicted. In addition a proof of concept of the code generator facility which generates the necessary infrastructure to quickly build new DSMLs is implemented and applied to the case study. The benefits of our approach are measured relying on three indicators: the reduced development time, the reused software components and the gain on learnability.

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“…According to the comparison in [32] it is one of the most fully-featured language workbenches. It is also used in practice for developing languages in domains such as computational biology [76], web applications [1], requirements engineering [104], insurance DSLs [106], safety engineering [71] as well as embedded software [103]. MPS has an active user community and continues to be developed by Jetbrains and other contributors.…”

Section: Jetbrains Mpsmentioning

confidence: 99%

“…More recently, DSLs have been used in domains that are not traditionally associated with formal, executable or analyzable languages. Examples include home automation [43], computational biology [76] and business applications [106] in the insurance industry. Because the users in such domains are not trained as programmers, they have different expectations of how languages and IDE should work; the diversity of linguistic styles and their syntax grows.…”

Section: Introductionmentioning

confidence: 99%

Lessons learned from developing mbeddr: a case study in language engineering with MPS

Voelter¹,

Kolb²,

Szabó

et al. 2017

Softw Syst Model

Self Cite

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Language workbenches are touted as a promising technology to engineer languages for use in a wide range of domains, from programming to science to business. However, not many real-world case studies exist that evaluate the suitability of language workbench technology for this task. This paper contains such a case study. In particular, we evaluate the development of mbeddr, a collection of integrated languages and language extensions built with the Jetbrains MPS language workbench. mbeddr consists of 81 languages, with their IDE support, 34 of them C extensions. The mbeddr languages use a wide variety of notations-textual, tabular, symbolic and graphical-and the C extensions are modular; new extensions can be added without changing the existing implementation of C. mbeddr's development has spanned 10 person years so far, and the tool is used in practice and continues to be developed. This makes mbeddr a meaningful case study of nontrivial size and complexity. The evaluation is centered around five research questions: language modularity, notational freedom and projectional editing, mechanisms for managing complexity, performance and scalability issues and the consequences for the development process. We draw generally positive conclusions; language engineering with MPS is ready for real-world use. However, we also identify a number of areas for improvement in the state of the art in language engineering in general, and in MPS in particular.

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Projecting a Modular Future

Cited by 19 publications

References 9 publications

A Study of Feature Scattering in the Linux Kernel

A Study of Feature Scattering in the Linux Kernel

A Practical Approach for Extending DSMLs by Composing their Metamodels

Lessons learned from developing mbeddr: a case study in language engineering with MPS

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