Automating Hardware and Software Evolution Analysis

Dougherty, Brian P.; White, Jules; Thompson, Chris; Schmidt, Douglas C.

doi:10.1109/ecbs.2009.22

Cited by 11 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hardware variability. Resarch work has been investigating the effect of hardware on software performance [4,9,11,14,27,28]. In particular, Valov et al [27,28] suggest that changing the hardware has reasonable impacts on software configurations since linear functions are highly accurate when reusing prediction models.…”

Section: Related Workmentioning

confidence: 99%

“…For Sections 2 and 3, we use the Legacy Performance Dataset [8]. Code and results are publicly available 9 . 6 Here, the performance property is the 99 𝑡ℎ percentile of allocated memory in Megabytes 7 See https://icpe2022.spec.org/tracks-and-submissions/data-challenge-track/ and https://www.daviddaly.me/2021/10/questions-on-icpe-2022-data-challenge.html for additional explanations 8 Download the dataset at https://zenodo.org/record/5138516 9 Companion repository : https://github.com/llesoil/icpe2022…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Beware of the Interactions of Variability Layers When Reasoning about Evolution of MongoDB

Lesoil

Acher

Blouin

et al. 2022

Companion of the 2022 ACM/SPEC International Conference on Performance Engineering

View full text Add to dashboard Cite

With commits and releases, hundreds of tests are run on varying conditions (e.g., over different hardware and workloads) that can help to understand evolution and ensure non-regression of software performance. We hypothesize that performance is not only sensitive to evolution of software, but also to different variability layers of its execution environment, spanning the hardware, the operating system, the build, or the workload processed by the software. Leveraging the MongoDB dataset, our results show that changes in hardware and workload can drastically impact performance evolution and thus should be taken into account when reasoning about evolution. An open problem resulting from this study is how to manage the variability layers in order to efficiently test the performance evolution of a software.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beware of the Interactions of Variability Layers When Reasoning about Evolution of MongoDB

Lesoil

Acher

Blouin

et al. 2022

Companion of the 2022 ACM/SPEC International Conference on Performance Engineering

View full text Add to dashboard Cite

show abstract

“…The proposed research described in the proposed C 2 ORES project builds upon our prior work [13,14,23,24,25,26] on cyber-physical systems that execute on multi-core architectures. An important outcome of our proposed project will be a tool-suite including model-driven engineering capabilities, heuristic/meta-heuristic algorithms, validation mechanisms based on emulation on realistic testbeds, and feedback capabilities that enable iterative refinements to the process.…”

Section: Figure 1: the C 2 Ores Workflow And Solution Approachmentioning

confidence: 99%

Cyber-Physical Multi-Core Optimization for Resource and Cache Effects (C2ORES)

Schmidt¹

2014

Self Cite

View full text Add to dashboard Cite

Using Government drawings, specifications, or other data included in this document for any purpose other than Government procurement does not in any way obligate the U.S. Government. The fact that the Government formulated or supplied the drawings, specifications, or other data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented invention that may relate to them. This report is the result of contracted fundamental research deemed exempt from public affairs security and policy review in accordance with SAF/AQR memorandum dated 10 Dec 08 and AFRL/CA policy clarification memorandum dated 16 Jan 09. This report is available to the general public, including foreign nationals. Copies may be obtained from the Defense Technical Information Center (DTIC) (http://www.dtic.mil).

show abstract

“…If software continues to evolve, it needs to be reorganized [2][3][4]. This is called refactoring and the frames occur during this time.…”

Section: Introductionmentioning

confidence: 99%

Application of Design Patterns in Process of Large-Scale Software Evolving

Wang¹,

Zhang²,

Li³

et al. 2010

JSEA

View full text Add to dashboard Cite

To search for the Design Patterns’ influence on the software, the paper abstracts the feature models of 9 kinds of classic exiting design patterns among the 23 kinds and describes the features with algorithm language. Meanwhile, searching for the specific structure features in the network, the paper designs 9 matching algorithms of the 9 kinds design patterns mentioned above to research on the structure of the design patterns in the software network. At last, the paper analyzes the evolving trends of the software scale and the application frequency of the 9 kinds of design patterns as the software evolves, and search for the rules how these design patterns are applied into 4 kinds of typical software

show abstract

Automating Hardware and Software Evolution Analysis

Abstract: Cost-effective software evolution is critical to many distributed real-time and embedded (DRE)

Cited by 11 publications

References 17 publications

Beware of the Interactions of Variability Layers When Reasoning about Evolution of MongoDB

Beware of the Interactions of Variability Layers When Reasoning about Evolution of MongoDB

Cyber-Physical Multi-Core Optimization for Resource and Cache Effects (C2ORES)

Application of Design Patterns in Process of Large-Scale Software Evolving

Contact Info

Product

Resources

About