Model-based performance risk analysis

Cortellessa, Vittorio; Goševa-Popstojanova, Katerina; Appukkutty, Kalaivani; Guedem, A.; Hassan, Alzubair; Elnaggar, R.; Abdelmoez, Walid; Ammar, H.H.

doi:10.1109/tse.2005.12

Cited by 55 publications

(36 citation statements)

References 17 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [53] the authors propose a severity assessment methodology which integrates Functional Failure Analysis (FFA) [1], FMEA and FTA to analyze the severity of failures of the architectural elements. In [34] the authors describe a risk analysis method of performance failures based on annotated UML models and [50] describes a method for analyzing architecture level UML models to identify the parts of the system which may give rise to risks. The method applies complexity analysis of components and connectors, expert judgments for severity analysis and Markov models to estimate the risk factor of the different system scenarios.…”

Section: Other Techniquesmentioning

confidence: 99%

On the Comprehension of Security Risk Scenarios

Hogganvik

Stølen

13th International Workshop on Program Comprehension (IWPC'05)

View full text Add to dashboard Cite

Section: Other Techniquesmentioning

confidence: 99%

On the Comprehension of Security Risk Scenarios

Hogganvik

Stølen

13th International Workshop on Program Comprehension (IWPC'05)

View full text Add to dashboard Cite

“…Performance of a legacy system is defined as the fulfillment of a system purpose without waste of resources, such as memory space and processor utilization, network bandwidth, time etc [11]. Performance risk component defines the loss associated with improper resource utilization and massive response time.…”

Section: Performance Risk Componentmentioning

confidence: 99%

Investigation of Quality and Functional Risk Issues in Reengineering Process of Legacy Software System

Rajavat¹,

Tokekar²

2014

IJPLA

View full text Add to dashboard Cite

show abstract

“…" Fluctuations due to lack of human interaction across designers ([91], [36] , [14]): A team composed of different engineers working on a project can, for lack of interaction, develop subsystems which do not fit together and which require a partial redesign with consequent mass and power fluctuations.…”

Section: State Of the Artmentioning

confidence: 99%

Statistical Risk Estimation for Communication System Design

Babuscia

Cheung

2013

IEEE Systems Journal

View full text Add to dashboard Cite

Spacecraft are complex systems that involve many subsystems and multiple relationships among them. The design of a spacecraft is an evolutionary process that starts from requirements and evolves over time. During this process, changes can affect mass and power at component, subsystem, and system level. Each spacecraft has to respect overall constraints in terms of mass and power. The current practice in system design deals with this problem by allocating margins to individual components and to individual subsystems. However, a statistical characterization of the fluctuations in mass and power of the overall system (i.e. the spacecraft) is missing. This lack can result in a risky spacecraft design that might not fit the mission constraints and requirements, or in a conservative design that might not fully utilize the available resources. This problem is especially challenging at the initial stage of the design, when high levels of uncertainty due to lack of knowledge are unavoidable.This research proposes a statistical approach to quantify the likelihood that the design of a spacecraft would meet the mission constraints in mass and power consumption, focusing on the initial stage of the design. Due to the complexity of the problem and the different expertise required to develop a complete risk model for a spacecraft design, the scope of this research is focused on risk estimation for a specific spacecraft subsystem: the communication subsystem. The current research aims to be a "proof of concept" of a risk-based design approach, which can then be further expanded to the design of other subsystems as well as to the whole spacecraft.The approach presented in this thesis includes a baseline communication system design tool, and a statistical characterization of the design risks through a combination of historical mission data and expert opinion. Different statistical techniques are explored to ensure that the amount of information extracted from data and expert opinion is maximized. Specifically, for statistics based on data, Kernel Density Estimator is selected as the preferred technique to extract probability densities from a database of previous space missions' components. Expert elicitation is generated 3 through a four-part model which quantifies experts' sensitivity to biases, and uses this measurement to compose properly the assessments from different experts. Finally, an optimization framework is developed to compare multiple possible design architectures, and to select the one that minimizes design objectives, like mass and power consumption, while minimizing the risk associated with the same metrics.Examples of missions are applied to validate the model. Results show that the statistical approach recognizes whether the initial estimate of the system is an overestimation or an underestimation, providing a valuable tool to measure the risk of a communication system at the initial state of the design. Specifically, statistics based on historical data and on expert elicitation allow the designer to siz...

show abstract

Model-based performance risk analysis

Cited by 55 publications

References 17 publications

On the Comprehension of Security Risk Scenarios

On the Comprehension of Security Risk Scenarios

Investigation of Quality and Functional Risk Issues in Reengineering Process of Legacy Software System

Statistical Risk Estimation for Communication System Design

Contact Info

Product

Resources

About