Journal Article10.1109/TSE.2005.12
Model-based performance risk analysis
Vittorio Cortellessa,Katerina Goseva-Popstojanova,Kalaivani Appukkutty,A. Guedem,Aisha Hassan,R. Elnaggar,W. Abdelmoez,Hany H. Ammar +7 more
TL;DR: The methodology elaborates annotated UML diagrams to estimate the performance failure probability and combines it with the failure severity estimate which is obtained using the functional failure analysis, and is able to determine risky scenarios as well as risky software components, and the analysis feedback can be used to improve the software design.
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Abstract: Performance is a nonfunctional software attribute that plays a crucial role in wide application domains spreading from safety-critical systems to e-commerce applications. Software risk can be quantified as a combination of the probability that a software system may fail and the severity of the damages caused by the failure. In this paper, we devise a methodology for estimation of performance-based risk factor, which originates from violations, of performance requirements, (namely, performance failures). The methodology elaborates annotated UML diagrams to estimate the performance failure probability and combines it with the failure severity estimate which is obtained using the functional failure analysis. We are thus able to determine risky scenarios as well as risky software components, and the analysis feedback can be used to improve the software design. We illustrate the methodology on an e-commerce case study using step-by step approach, and then provide a brief description of a case study based on large real system.
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Citations
Timing-Failure Risk Assessment of UML Design Using Time Petri Net Bound Techniques
TL;DR: The method customizes the Australian standard risk management process, where the system context is the UML-based software specification, enriched with standard MARTE profile annotations to capture nonfunctional system properties.
61
A Probabilistic Software Risk Assessment and Estimation Model for Software Projects
Chandan Kumar,Dilip Kumar Yadav +1 more
TL;DR: A probabilistic software risk estimation model is proposed using Bayesian Belief Network (BBN) that focuses on the top software risk indicators for risk assessment in software development projects that will help in finding the correlation between risk factors and project outcome.
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QoS assessment via stochastic analysis
Simona Bernardi,José Merseguer +1 more
TL;DR: Using a stochastic modeling approach based on the Unified Modeling Language and enriched with annotations that conform to the UML profile for schedulability performance, and time, the authors propose a method for assessing quality of service (QoS) in fault-tolerant distributed systems.
29
Search-based fairness testing for regression-based machine learning systems
A. K. A. Perera,Aldeida Aleti,Chakkrit Tantithamthavorn,Jirayus Jiarpakdee,Burak Turhan,Lisa Kuhn,Katie Walker +6 more
TL;DR: In this paper , the authors proposed a new fairness measure for regression-based ML systems, and a novel search-based fairness testing (SBFT) approach for testing regressionbased machine learning systems.
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