The Architecture Tradeoff Analysis Method.

Kazman, Rick; Klein, Mark; Barbacci, Mario R.; Longstaff, T. G.; Lipson, Howard F.

doi:10.21236/ada350761

Cited by 209 publications

(149 citation statements)

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“…Some researchers have made a correspondence between the need for quality engineering in software product lines and quality assurance in software architectures. For instance, the Holistic Product Line Architecture Assessment (HoPLAA) is an adaptation of the Architecture Tradeoff Analysis Method (ATAM) (Kazman et al 1998) for the area of software product lines (Olumofin and Mišić 2007). As another example, the authors of Etxeberria and Sagardui (2008) have extended FeatuRSEB (Griss et al 1998) with the quality attribute utility tree introduced in ATAM ( Kazman et al 1998).…”

Section: Related Workmentioning

confidence: 99%

“…For instance, the Holistic Product Line Architecture Assessment (HoPLAA) is an adaptation of the Architecture Tradeoff Analysis Method (ATAM) (Kazman et al 1998) for the area of software product lines (Olumofin and Mišić 2007). As another example, the authors of Etxeberria and Sagardui (2008) have extended FeatuRSEB (Griss et al 1998) with the quality attribute utility tree introduced in ATAM ( Kazman et al 1998). Similarly, the FODA feature modeling formalism (Kang et al 1990) has been combined with the i* goaloriented modeling framework (Yu and Mylopoulos 1994) to form the F-SIG framework to address quality attributes in feature models (Jarzabek et al 2006).…”

Section: Related Workmentioning

confidence: 99%

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Assessing the maintainability of software product line feature models using structural metrics

2011

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A software product line is a unified representation of a set of conceptually similar software systems that share many common features and satisfy the requirements of a particular domain. Within the context of software product lines, feature models are treelike structures that are widely used for modeling and representing the inherent commonality and variability of software product lines. Given the fact that many different software systems can be spawned from a single software product line, it can be anticipated that a low-quality design can ripple through to many spawned software systems. Therefore, the need for early indicators of external quality attributes is recognized in order to avoid the implications of defective and low-quality design during the late stages of production. In this paper, we propose a set of structural metrics for software product line feature models and theoretically validate them using valid measurement-theoretic principles. Further, we investigate through controlled experimentation whether these structural metrics can be good predictors (early indicators) of the three main subcharacteristics of maintainability: analyzability, changeability, and understandability. More specifically, a four-step analysis is conducted: (1) investigating whether feature model structural metrics are correlated with feature model maintainability through the employment of classical statistical correlation techniques; (2) understanding how well each of the structural metrics can serve as discriminatory references for maintainability; (3) identifying the sufficient set of structural metrics for evaluating each of the subcharacteristics of maintainability; and (4) evaluating how well different prediction models based on the proposed structural metrics can perform in indicating the maintainability of a feature model. Results obtained from the controlled experiment support the idea that useful prediction models can be built for the purpose of evaluating feature model maintainability using early structural metrics. Some of the structural metrics show significant correlation with the subjective perception of the subjects about the maintainability of the feature models.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Assessing the maintainability of software product line feature models using structural metrics

2011

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“…The existing methods are essentially design methods and should therefore be enhanced with a more elaborated analysis part. Another set of heuristics based methods concentrate specifically on the analysis of software architecture, such as the Scenario-Based Architecture Analysis Method (SAAM) [28], SAAM founded on Complex Scenarios (SAAMCS) [29], SAAM extended by its integration into the domain (ESAAMI) [30] and the Architecture Tradeoff Analysis Method (ATAM) [31]. These methods have different goals, and are focused on multiple software attributes such as modifiability, flexibility, and performance.…”

Section: Overview Of Reliability and Availability Prediction Approachesmentioning

confidence: 99%

Survey of reliability and availability prediction methods from the viewpoint of software architecture

2007

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Many future software systems will be distributed across a network, extensively providing different kinds of services for their users. These systems must be highly reliable and provide services when required. Reliability and availability must be engineered into software from the onset of its development, and potential problems must be detected in the early stages, when it is easier and less expensive to implement modifications. The software architecture design phase is the first stage of software development in which it is possible to evaluate how well the quality requirements are being met. For this reason, a method is needed for analyzing software architecture with respect to reliability and availability. In this paper, we define a framework for comparing reliability and availability analysis methods from the viewpoint of software architecture. Our contribution is the comparison of the existing analysis methods and techniques that can be used for reliability and availability prediction at the architectural level. The objective is to discover which methods are suitable for the reliability and availability prediction of today's complex systems, what are the shortcomings of the methods, and which research activities need to be conducted in order to overcome these identified shortcomings. The comparison reveals that none of the existing methods entirely fulfill the requirements that are defined in the Communicated by Oystein Haugen.A. Immonen (B) · E. Niemelä

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“…There may not exist a perfectly fitting architecture or a totally unsuitable one, but there are surely architectures which achieve a more or less suitable solution for a given problem and usage. A number of methods and techniques were developed for the evaluation of software architectures, for example: ATAM (Architecture Trade-off Analysis Method (Kazman, 1998)), SAAM (Software Architecture Analysis Method (De Simone and Kazman, 1995)), or ARID (Active Reviews for Intermediate Designs (Clements, 2000)). However, for our purpose we mainly focus on two questions: (i) how to express the performance-related information in a context-awareness (ambient) intelligent application and (ii) how to assess the performance of the application during its execution.…”

Section: Performance Assessment In Ambient Intelligence (Paai)mentioning

confidence: 99%

Performance assessment of intelligent distributed systems through software performance ontology engineering (SPOE)

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In the computer science community there is a growing interest in the field of Ambient Intelligent Systems. This systems surround their human users with computing and networking technology unobtrusively embedded in their environment. This technology is aimed to provide the users with useful information and to take action to make the environment more convenient for them. As the number of users increases the resources that make Ambient Intelligence possible can be easily saturated making the system unstable and projecting an image of poor QoS to the users. The main goal of this paper is to provide the means for the Ambient Intelligent Systems to monitor themselves and take corrective action automatically if performance starts to drop. Our approach uses a Performance Ontology that structures the knowledge about Software Performance Engineering, and a reasoning engine that acts like an expert system with the Performance Ontology as its foundation. The case study at the end shows the applicability of the developed techniques.

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The Architecture Tradeoff Analysis Method.

Abstract: The ideas and findings in this report should not be construed as an official DoD position. It is published in the interest of scientific and technical information exchange.

Cited by 209 publications

References 0 publications

Assessing the maintainability of software product line feature models using structural metrics

Assessing the maintainability of software product line feature models using structural metrics

Survey of reliability and availability prediction methods from the viewpoint of software architecture

Performance assessment of intelligent distributed systems through software performance ontology engineering (SPOE)

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