Preserving Software Quality Characteristics from Requirements Analysis to Architectural Design

Schmidt, Holger; Wentzlaff, Ina

doi:10.1007/11966104_14

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…For these two data sets, λ max is 932,575 and 992, respectively. We use the logistic regression code from L1General (Schmidt, 2006) as the inner iteration algorithm in this set of experiments. Figure 5 plots the running time at different λ values for dynamic screening, BLITZ, and SAIF.…”

Section: Results For Logistic Regressionmentioning

confidence: 99%

Safe Active Feature Selection for Sparse Learning

Ren,

Huang,

Huang

et al. 2018

Preprint

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We present safe active incremental f eature selection (SAIF) to scale up the computation of LASSO solutions. SAIF does not require a solution from a heavier penalty parameter as in sequential screening or updating the full model for each iteration as in dynamic screening. Different from these existing screening methods, SAIF starts from a small number of features and incrementally recruits active features and updates the significantly reduced model. Hence, it is much more computationally efficient and scalable with the number of features. More critically, SAIF has the safe guarantee as it has the convergence guarantee to the optimal solution to the original full LASSO problem. Such an incremental procedure and theoretical convergence guarantee can be extended to fused LASSO problems. Compared with state-of-the-art screening methods as well as working set and homotopy methods, which may not always guarantee the optimal solution, SAIF can achieve superior or comparable efficiency and high scalability with the safe guarantee when facing extremely high dimensional data sets. Experiments with both synthetic and real-world data sets show that SAIF can be up to 50 times faster than dynamic screening, and hundreds of times faster than computing LASSO or fused LASSO solutions without screening.

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Section: Results For Logistic Regressionmentioning

confidence: 99%

Safe Active Feature Selection for Sparse Learning

Ren,

Huang,

Huang

et al. 2018

Preprint

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“…The approach does not consider the elicitation of the initial set of security requirements (only dependent security requirements are elicited), a concrete attacker model, conflicting requirements, nor interaction of security and other non-functional requirements. However, a first paper addressing the latter two issues has already been published [84].…”

Section: Abuse Framesmentioning

confidence: 99%

A comparison of security requirements engineering methods

et al. 2009

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This paper presents a conceptual framework for security engineering, with a strong focus on security requirements elicitation and analysis. This conceptual framework establishes a clear-cut vocabulary and makes explicit the interrelations between the different concepts and notions used in security engineering. Further, we apply our conceptual framework to compare and evaluate current security requirements engineering approaches, such as the Common Criteria, Secure Tropos, SREP, MSRA, as well as methods based on UML and problem frames. We review these methods and assess them according to different criteria, such as the general approach and scope of the method, its validation, and quality assurance capabilities. Finally, we discuss how these methods are related to the conceptual framework and to one another.

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“…And as a last difference, a refinement to implementable architectures is not considered. Nevertheless, the papers by Choppy & Hatebur & Heisel (2005 and as well as the idea to systematically preserve quality requirements from early requirements engineering to software design presented by Schmidt & Wentzlaff (2006) constitute the basis for the enhancements presented in this chapter.…”

Section: Security Architecturesmentioning

confidence: 99%

“…In the case that it is relevant, the conflict must be resolved by modifying or prioritizing the requirements. An example of relaxing security requirements for the benefit of usability requirements can be found in Schmidt & Wentzlaff (2006). This step can result in modified sets of security requirements and concretized security requirements.…”

Section: Step 6 -Analyze Possible Conflictsmentioning

confidence: 99%

A Pattern-Based Method to Develop Secure Software

Schmidt

Hatebur

Heisel

2011

Software Engineering for Secure Systems

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The authors present a security engineering process based on security problem frames and concretized security problem frames. Both kinds of frames constitute patterns for analyzing security problems and associated solution approaches. They are arranged in a pattern system that makes dependencies between them explicit. The authors describe step-by-step how the pattern system can be used to analyze a given security problem and how solution approaches can be found. Afterwards, the security problems and the solution approaches are formally modeled in detail. The formal models serve to prove that the solution approaches are correct solutions to the security problems. Furthermore, the formal models of the solution approaches constitute a formal specification of the software to be developed. Then, the specification is implemented by generic security components and generic security architectures, which constitute architectural patterns. Finally, the generic security components and the generic security architecture that composes them are refined and the result is a secure software product built from existing and/or tailor-made security components.

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Preserving Software Quality Characteristics from Requirements Analysis to Architectural Design

Cited by 12 publications

References 4 publications

Safe Active Feature Selection for Sparse Learning

Safe Active Feature Selection for Sparse Learning

A comparison of security requirements engineering methods

A Pattern-Based Method to Develop Secure Software

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