Scaling step-wise refinement

Batory, Don; Sarvela, Jacob Neal; Rauschmayer, Axel

doi:10.1109/tse.2004.23

Cited by 492 publications

(151 citation statements)

References 36 publications

Supporting

Mentioning

148

Contrasting

Unclassified

Order By: Relevance

“…There have been attempts to bring SoC down to the design and implementation levels. Aspect-oriented programming (AOP) [30], multi-dimensional SoC (MDSOC) from IBM [31], feature-oriented programming (FOP) [32], and colored IDE (CIDE) [33] are among the most widely published of such techniques. Among these techniques, AOP has been widely used.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Feature-oriented programming (FOP) is a software development methodology in which features are first-class citizens in the software architecture [27,28]. FOP decomposes applications into a set of features that together provide the requisite functionality.…”

Section: Feature-oriented Programming and Mixin-layersmentioning

confidence: 99%

Reusable software components for accelerator-based clusters

Rafique

Butt

Tilevich

2011

Journal of Systems and Software

View full text Add to dashboard Cite

The emerging accelerator-based heterogeneous clusters, comprising specialized processors such as the IBM Cell and GPUs, have exhibited excellent price to performance ratio as well as high energy-efficiency. However, developing and maintaining software for such systems is fraught with challenges, especially for modern high-performance computing (HPC) applications that can benefit the most from leveraging accelerators. If accelerator-based clusters are to deliver on their initial promise to provide a viable and cost-effective HPC solution to researchers and practitioners, one must find a software solution to lower the barrier to entry for the average user. In this paper, we investigate how a software component based approach can be used to provide a reusable and adaptable architecture for executing HPC tasks on accelerator-based clusters. In our implementation, we leverage the lessons from the software engineering research for component-based layered architectures. Our results indicate that the complexity of developing and maintaining accelerator-based cluster software can be as effectively tamed by solid software engineering approaches as that of software in more traditional domains. Specifically, we were able to reuse 83.6% of our implementation code across different architectures and resource configurations, while achieving the overall execution performance only 1.5% off that of an optimally hand-tuned, albeit non-reusable version.

show abstract

“…There is a long history of creating and implementing GenVoca product lines in different domains (e.g. [8,10]). We review its key ideas and then explain our model of MapStats.…”

Section: A Transformation-based Model Of Product Linesmentioning

confidence: 99%

“…That is, when a feature is added to a program, new classes can be added, new members can be added to existing classes, and existing methods can be modified. There is a host of technologies-including aspects, languages for object-oriented collaborations [10], and rewrite rules in program transformation systems [11]-that can modularize and implement features as transformations. In MapStats, features not only refine JavaScript programs by adding new classes, methods and statements, but also new graphics elements can be added to SVG programs as well.…”

Section: Genvocamentioning

confidence: 99%

“…The lone base program is the Java preprocessor (Java), and the remaining features add constructs to the Java language, such as code quote and unquote (Ast) [5], a form of hygienic macros called generation scoping (Gscope) [46], state machine declarations (Sm), adding refinements to Java classes and interfaces (CompJava), and adding refinements to state machines (CompSm) [10]. The base feature (Java) defined a complete Java language; the remaining features extend this language.…”

Section: Grammarsmentioning

confidence: 99%

See 1 more Smart Citation

Lifting transformational models of product lines: a case study

et al. 2009

View full text Add to dashboard Cite

Model driven engineering (MDE) of software product lines (SPLs) merges two increasing important paradigms that synthesize programs by transformation. MDE creates programs by transforming models, and SPLs elaborate programs by applying transformations called features. In this paper, we present the design and implementation of a transformational model of a product line of scalar vector graphics and JavaScript applications. We explain how we simplified our implementation by lifting selected features and their compositions from our original product line (whose implementations were complex) to features and their compositions of another product line (whose specifications were simple). We used operators to map higher-level features and their compositions to their lower-level counterparts. Doing so exposed commuting relationships among feature compositions in both product lines that helped validate our model and implementation.

show abstract

Scaling step-wise refinement

Cited by 492 publications

References 36 publications

Managing Big Clones to Ease Evolution: Linux Kernel Example

Managing Big Clones to Ease Evolution: Linux Kernel Example

Reusable software components for accelerator-based clusters

Lifting transformational models of product lines: a case study

Contact Info

Product

Resources

About