Combining the best attributes of qualitative and quantitative risk management tool support

Feather, Martin S.; Cornford, Steven L.; Larson, Timothy W.

doi:10.1109/ase.2000.873685

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…They emphasize the importance of tool support, e.g. for visualization (see also in Feather et al 2000a, Feather et al 2000b) and the involvement of experts who cover a wide spectrum of knowledge (Feather and Cornford 2003): "Typical DDP applications have involved 10-20 experts drawn from the disciplines of mission science, project planning, software and hardware engineering, quality assurance, testing, risk management, etc." Their positive experiences from many case studies (e.g.…”

Section: Requirements Prioritization Based On Riskmentioning

confidence: 99%

Practical challenges of requirements prioritization based on risk estimation

Herrmann

Paech

2009

Empir Software Eng

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Requirements prioritization and risk estimation are known to be difficult. However, so far, risk-based requirements prioritization has not been investigated empirically and quantitatively. In two quantitative experiments, we explored practical challenges and needs of risk estimations in general and of our method MOQARE specifically. In the first experiment, ten students made individual estimations. In the second one, twenty-four students estimated risks in seven moderated groups. The students prioritized the same requirements with different methods (risk estimation and ranking). During the first experiment, we identified factors which influence the quality of the prioritization. In the second experiment, the results of the risk estimation could be improved by discussing risk estimations in a group of experts, gathering risk statistics, and defining requirements, risks and prioritization criteria more tangibly. This first quantitative study on risk-based requirements prioritization helps to understand the practical challenges of this task and thus can serve as a basis for further research on this topic.

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Section: Requirements Prioritization Based On Riskmentioning

confidence: 99%

Practical challenges of requirements prioritization based on risk estimation

Herrmann

Paech

2009

Empir Software Eng

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“…Figure 14 shows a third simulation study with TAR2. Analysts at the NASA Jet Propulsion Laboratory debate satellite design by building a semantic network connecting design decisions to satellite requirements (Feather et al 2000). Each edge is annotated with the numeric cost and benefits of taking some action.…”

Section: Simulation Studiesmentioning

confidence: 99%

Just enough learning (of association rules): the TAR2 “Treatment” learner

2006

Artif Intell Rev

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An over-zealous machine learner can automatically generate large, intricate, theories which can be hard to understand. However, such intricate learning is not necessary in domains that lack complex relationships. A much simpler learner can suffice in domains with narrow funnels; i.e. where most domain variables are controlled by a very small subset. Such a learner is TAR2: a weighted-class minimal contrast-set association rule learner that utilizes confidence-based pruning, but not support-based pruning. TAR2 learns treatments; i.e. constraints that can change an agent's environment. Treatments take two forms. Controller treatments hold the smallest number of conjunctions that most improve the current state of the system. Monitor treatments hold the smallest number of conjunctions that best detect future faulty system behavior. Such treatments tell an agent what to do (apply the controller) and what to watch for (the monitor conditions) within the current environment. Because TAR2 generates very small theories, our experience has been that users prefer its tiny treatments. The success of such a simple learner suggests that many domains lack complex relationships.

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Quantitative risk-based requirements reasoning

Feather

Cornford

2003

Requirements Engineering

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Combining the best attributes of qualitative and quantitative risk management tool support

Cited by 6 publications

References 5 publications

Practical challenges of requirements prioritization based on risk estimation

Practical challenges of requirements prioritization based on risk estimation

Just enough learning (of association rules): the TAR2 “Treatment” learner

Quantitative risk-based requirements reasoning

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