Combining Goal Models, Expert Elicitation, and Probabilistic Simulation for Qualification of New Technology

Sabetzadeh, Mehrdad; Falessi, Davide; Briand, Lionel C.; Alesio, Stefano Di; McGeorge, Dag; Ahjem, Vidar; Borg, Jonas

doi:10.1109/hase.2011.22

Cited by 22 publications

(11 citation statements)

References 10 publications

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“…This is the most frequent quantitative technique in the literature for evidence assessment. The Modus approach [PS137], which combines quantitative assessment with formal argumentation structures. The approach is based on quantitative reasoning that uses goal models (KAOS), expert elicitation, and probabilistic simulation for assessing the overall goal of a safety case.…”

Section: Quantitative Assessment (10%)mentioning

confidence: 99%

An extended systematic literature review on provision of evidence for safety certification

Nair

Vara

Sabetzadeh³

et al. 2014

Information and Software Technology

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Abstract:Context: Critical systems in domains such as aviation, railway, and automotive are often subject to a formal process of safety certification. The goal of this process is to ensure that these systems will operate safely without posing undue risks to the user, the public, or the environment. Safety is typically ensured via complying with safety standards. Demonstrating compliance to these standards involves providing evidence to show that the safety criteria of the standards are met. Objective:In order to cope with the complexity of large critical systems and subsequently the plethora of evidence information required for achieving compliance, safety professionals need in-depth knowledge to assist them in classifying different types of evidence, and in structuring and assessing the evidence. This paper is a step towards developing such a body of knowledge that is derived from a largescale empirically rigorous literature review. Method:We use a Systematic Literature Review (SLR) as the basis for our work. The SLR builds on 218 peer-reviewed studies, selected through a multi-stage process, from 4,963 studies published between 1990 and 2012. Results:We develop a taxonomy that classifies the information and artefacts considered as evidence for safety. We review the existing techniques for safety evidence structuring and assessment, and further study the relevant challenges that have been the target of investigation in the academic literature. We analyse commonalities in the results among different application domains and discuss implications of the results for both research and practice. Conclusion:The paper is, to our knowledge, the largest existing study on the topic of safety evidence. The results are particularly relevant to practitioners seeking a better grasp on evidence requirements as well as to researchers in the area of system safety. As a major finding of the review, the results strongly suggest the need for more practitioner-oriented and industry-driven empirical studies in the area of safety certification.

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Section: Quantitative Assessment (10%)mentioning

confidence: 99%

An extended systematic literature review on provision of evidence for safety certification

Nair

Vara

Sabetzadeh³

et al. 2014

Information and Software Technology

Self Cite

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“…[16,12]). Obstacle likelihood and criticality may be determined quantitatively over obstacle refinement trees and goal refinement trees, respectively [12,31].…”

Section: Discussion and Related Workmentioning

confidence: 99%

Risk-driven revision of requirements models

Alrajeh

Lamsweerde

Kramer

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

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Requirements incompleteness is often the result of unanticipated adverse conditions which prevent the software and its environment from behaving as expected. These conditions represent risks that can cause severe software failures. The identification and resolution of such risks is therefore a crucial step towards requirements completeness. Obstacle analysis is a goal-driven form of risk analysis that aims at detecting missing conditions that can obstruct goals from being satisfied in a given domain, and resolving them. This paper proposes an approach for automatically revising goals that may be under-specified or (partially) wrong to resolve obstructions in a given domain. The approach deploys a learning-based revision methodology in which obstructed goals in a goal model are iteratively revised from traces exemplifying obstruction and non-obstruction occurrences. Our revision methodology computes domain-consistent, obstruction-free revisions that are automatically propagated to other goals in the model in order to preserve the correctness of goal models whilst guaranteeing minimal change to the original model. We present the formal foundations of our learning-based approach, and show that it preserves the properties of our formal framework. We validate it against the benchmarking case study of the London Ambulance Service.

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“…For risk identification, scenario-based heuristics are available [2,29] as well as goal-oriented formal techniques [1,14]. For risk assessment, various kinds of quantitative techniques are available [3,5,8,25]. For risk control, the only work on countermeasure exploration is [14] where the obstacle resolution tactics mentioned in this paper are described.…”

Section: Related Workmentioning

confidence: 99%

“…For obstacle assessment, likelihoods and criticalities may be determined quantitatively by calculations over obstacle refinement trees and goal refinement trees, respectively; such calculations call for probabilistic extensions to cope with probabilistic goals and obstacles [5,25]. For obstacle resolution, operators encoding risk control tactics were proposed to explore alternative resolutions -such as avoid obstacle, reduce obstacle likelihood, mitigate obstacle, weaken goal, substitute goal, restore goal, or substitute agent [14].…”

Section: Introductionmentioning

confidence: 99%

Integrating exception handling in goal models

Cailliau

Lamsweerde

2014

2014 IEEE 22nd International Requirements Engineering Conference (RE)

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Missing requirements are known to be among the major sources of software failure. Incompleteness often results from poor anticipation of what could go wrong with an over-ideal system. Obstacle analysis is a model-based, goalanchored form of risk analysis aimed at identifying, assessing and resolving exceptional conditions that may obstruct the behavioral goals of the target system. The obstacle resolution step is obviously crucial as it should result in more adequate and more complete requirements. In contrast with obstacle identification and assessment, however, this step has little support beyond a palette of resolution operators encoding tactics for producing isolated countermeasures to single risks. In particular, there is no single clue to date as to where and how such countermeasures should be integrated within a more robust goal model. To address this problem, the paper describes a systematic technique for integrating obstacle resolutions as countermeasure goals into goal mod... Obstacle analysis is a model-based, goal-anchored form of risk analysis aimed at identifying, assessing and resolving exceptional conditions that may obstruct the behavioral goals of the target system. The obstacle resolution step is obviously crucial as it should result in more adequate and more complete requirements. In contrast with obstacle identification and assessment, however, this step has little support beyond a palette of resolution operators encoding tactics for producing isolated countermeasures to single risks. In particular, there is no single clue to date as to where and how such countermeasures should be integrated within a more robust goal model. To address this problem, the paper describes a systematic technique for integrating obstacle resolutions as countermeasure goals into goal models. The technique is shown to guarantee progress towards a complete goal model; it preserves the correctness of refinements in the overall model; and keeps the original, ideal model visible to avoid cluttering the latter with a combinatorial blow-up of exceptional cases. To allow for this, the goal specification language is slightly extended in order to capture exceptions to goals seperately and distinguish normal situations from exceptional ones. The proposed technique is evaluated on a non-trivial ambulance dispatching system.

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Combining Goal Models, Expert Elicitation, and Probabilistic Simulation for Qualification of New Technology

Cited by 22 publications

References 10 publications

An extended systematic literature review on provision of evidence for safety certification

An extended systematic literature review on provision of evidence for safety certification

Risk-driven revision of requirements models

Integrating exception handling in goal models

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