DoSAM – Domain-Specific Software Architecture Comparison Model

Bergner, Klaus; Rausch, Andreas; Sihling, Marc; Ternité, Thomas

doi:10.1007/11558569_3

Cited by 11 publications

(8 citation statements)

References 3 publications

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“…The prominent scenario-based architecture assessment methods for operational quality attributes are the Architecture Trade-off Analysis Method (ATAM) [30,6], Scenario-Based software Architecture Re-engineering (SBAR) [8], Software architecture Comparison Analysis Method (SCAM) [52], Domain Specific software Architecture comparison Model (DoSAM) [10], and Pattern-Based Architecture Reviews (PBAR) [22].…”

Section: Software Architecturementioning

confidence: 99%

“…ISO 26262 standard does not prescribe methods to assess the functional safety of automotive architecture. Many approaches to assess architectures with respect to quality attributes have emerged in the software architecture domain in the past three decades [4,20,30,6,8,52,10,22]. However, only some of these methods are designed for operational quality attributes like performance (in contrast to development quality attributes like maintainability) [12,4].…”

Section: Introductionmentioning

confidence: 99%

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A Functional Safety Assessment Method for Cooperative Automotive Architecture

Kochanthara¹,

Rood²,

Saberi³

et al. 2021

Preprint

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The scope of automotive functions has grown from a single vehicle as an entity to multiple vehicles working together as an entity, referred to as cooperative driving. The current automotive safety standard, ISO 26262, is designed for single vehicles. With the increasing number of cooperative driving capable vehicles on the road, it is now imperative to systematically assess the functional safety of architectures of these vehicles. Many methods are proposed to assess architectures with respect to different quality attributes in the software architecture domain, but to the best of our knowledge, functional safety assessment of automotive architectures is not explored in the literature. We present a method, that leverages existing research in software architecture and safety engineering domains, to check whether the functional safety requirements for a cooperative driving scenario are fulfilled in the technical architecture of a vehicle. We apply our method on a real-life academic prototype for a cooperative driving scenario, platooning, and discuss our insights.

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Section: Software Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Functional Safety Assessment Method for Cooperative Automotive Architecture

Kochanthara¹,

Rood²,

Saberi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Scenario based evaluation methods identify scenarios in close interaction with the stakeholders and systematically investigate the software architecture based on these scenarios. Some of the examples are Software Architecture Analysis Method (SAAM), Architecture Trade-off Analysis Method (ATAM), Family Architecture Analysis Method (FAAM), and Domain-Specific Software Architecture Comparison Model (DoSAM) (Ionita, 2002) (Kazman R. a., 1994), (Kazman R. a., 1998), (Bergner, 2005) .…”

Section: Related Workmentioning

confidence: 99%

“…ATAM is another evaluation method for assessing quality attributes such as modifiability, portability, extensibility, and integrality. DoSAM is another scenario-based evaluation method designed to assess software quality attributes like performance, scalability, and availability (Bergner, 2005).…”

Section: Related Workmentioning

confidence: 99%

Empirical Evaluation of Big Data Analytics using Design of Experiment: Case Studies on Telecommunication Data

Singh¹,

Liu²,

Ding³

et al. 2016

STBD

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Data analytics involves the process of data collection, data analysis, and report generation. Data mining workflow tools usually orchestrate this process. The data analysis step in this process further consists a series of machine learning algorithms. There exists a variety of data mining tools and machine learning algorithms. Each tool or algorithm has its own set of features that become factors to affect both functional and nonfunctional attributes of the system of data analytics. Given domain-specific requirements of data analytics, understanding the effects of these factors and their combinations provide a guideline of selecting workflow tools and machine learning algorithms. In this paper, we develop an empirical evaluation method based on the principle of Design of Experiment. We apply this method to evaluate data mining tools and machine learning algorithms towards building big data analytics for telecommunication monitoring data. Two case studies are conducted to provide insights of relations between the requirements of data analytics and the choice of a tool or algorithm in the context of data analysis workflows. The demonstration also shows that our evaluation method can facilitate the replication of this evaluation study, and can conveniently be expanded for evaluating other tools and algorithms.

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“…Several approaches deal with the evaluation of software architecture. Ali Babar and Gorton [6], Ali Babar et al [7], Bergner et al [8], Dobrica and Niemelä [9], Ionita et al [10], and Grunske [11]) have compared the different approaches in surveys. According to Abowd et al [12], architecture evaluation methods are classified into qualitative, quantitative, and hybrid methods.…”

Section: Introductionmentioning

confidence: 99%

Architecture Potential Analysis: A Closer Look inside Architecture Evaluation

Florentz

Huhn

2007

JSW

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The share of software in embedded systems has been growing permanently in the recent years. Thus, software architecture as well as its evaluation have become important parts of the development of embedded systems to describe, assess, and assure sound architecture as basis for high quality systems. Furthermore, design space exploration can be based on architecture evaluation. To achieve an efficient exploration process, architectural decisions need to be taken into account as part of the architecture. In this paper, a method for analyzing architecture potential on the basis of dependencies between quality attributes is presented and applied. An explicit representation and correlation of such dependencies provides decision support for architectural concerns. Not only can suboptimal decisions be avoided but rather valuable options are highlighted. Besides the quality of an architecture, knowledge of how to achieve and even improve the quality can be analyzed. The latter is the concern of architecture potential analysis presented in this paper. Furthermore, architectural decisions can be documented and will be traceable and justifiable with respect to the development rationale. The ongoing development process can then be based on dependable and well documented architectural decisions. The predictability of change impacts is increased. Thus, time and costs can be saved by avoiding suboptimal changes.

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DoSAM – Domain-Specific Software Architecture Comparison Model

Cited by 11 publications

References 3 publications

A Functional Safety Assessment Method for Cooperative Automotive Architecture

A Functional Safety Assessment Method for Cooperative Automotive Architecture

Empirical Evaluation of Big Data Analytics using Design of Experiment: Case Studies on Telecommunication Data

Architecture Potential Analysis: A Closer Look inside Architecture Evaluation

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