Uncertainty, risk, and information value in software requirements and architecture

Letier, Emmanuel; Stefan, David B.; Barr, Earl T.

doi:10.1145/2568225.2568239

Cited by 79 publications

(74 citation statements)

References 67 publications

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“…Decision makers are informed of possible trade-offs among conflicting objectives by visualising the Pareto optimal solutions generated by these quantitative methods. However, little work has been done to model design time uncertainties, interpret the consequences of those uncertainties, and support decision makers in analysing the inherent characteristics of model parameters [14], [26], [27], [41]. The decisions have to be made under incomplete knowledge about software project.…”

Section: Discussionmentioning

confidence: 99%

“…There are three main categories exact optimisations found in the literature. They are Integer linear programming [25], dynamic programming [26], and exhaustive search [27].…”

Section: A Requirement Selection and Optimisationmentioning

confidence: 99%

“…Thereby, the outcome of their approach is a conservative robust solution, which can avoid the impact of uncertainty in the worst scenario. In 2014, Leiter et al applied statistical decision theory to illustrate the expected information value of model parameters based on [41] to offer further decision suggestions [27]. To overcome the limitations of approximate meta-heuristic algorithms, the exhaustive search was adapted to explore the full solution space.…”

Section: B Uncertainty Handling In Requirements Selection and Optimimentioning

confidence: 99%

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Exact Analysis for Next Release Problem

Li¹

2016

2016 IEEE 24th International Requirements Engineering Conference (RE)

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Abstract-In software engineering, determining the set of requirements to implement in the next release is a critical foundation for the success of a project. Inappropriately including or excluding requirements may result in products that fail to satisfy stakeholders' needs, and might cause loss of revenue. In the meantime, uncertainty is characterised by incomplete understanding. It is inevitable in the early phase of requirements engineering, and could lead to unsound requirement decisions. To ease the impact of uncertainty in the software development process, it is important to provide techniques that explicitly manage uncertainty in requirements analysis and optimisation.This proposed research aims to provide a decision support framework for analysing uncertainty in requirements selection and optimisation. The proposed research involves three stages. Firstly, a simulation optimisation technique is introduced to model requirements uncertainty in requirements optimisation. Then, an exact technique is designed to eliminate the algorithmic uncertainty. Lastly, a probabilistic uncertainty analysis is applied to help the decision maker to understand requirement uncertainty propagation and the characteristics of requirements in requirements selection process.

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Section: Discussionmentioning

confidence: 99%

“…There are three main categories exact optimisations found in the literature. They are Integer linear programming [25], dynamic programming [26], and exhaustive search [27].…”

Section: A Requirement Selection and Optimisationmentioning

confidence: 99%

Section: B Uncertainty Handling In Requirements Selection and Optimimentioning

confidence: 99%

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Exact Analysis for Next Release Problem

Li¹

2016

2016 IEEE 24th International Requirements Engineering Conference (RE)

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“…It provides a well structured procedure to evaluate an architecture's fitness taking into account a set of non-dominant quality attributes (in the sense that improving one attribute will implicitly worsen another). ATAM has been successfully used for analysing software architectures in cloud computing [30], with a special focus on the security aspect [13], for investigating the design of service-oriented systems for serious games [8] as well as a starting point for bespoke approaches considering the impact of uncertainty on software requirements and architectures [29], the importance of enterprise information systems availability [33] or the sustainability of software architectures [50]. When applied to the Aviator domain, ATAM enabled the identification and management of the following factors.…”

Section: Aviator Architecture Evaluationmentioning

confidence: 99%

An architecture for the autonomic curation of crowdsourced knowledge

et al. 2017

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Human knowledge curators are intrinsically better than their digital counterparts at providing relevant answers to queries. That is mainly due to the fact that an experienced biological brain will account for relevant community expertise as well as exploit the underlying connections between knowledge pieces when offering suggestions pertinent to a specific question, whereas most automated database managers will not. We address this problem by proposing an architecture for the autonomic curation of crowdsourced knowledge, that is underpinned by semantic technologies. The architecture is instantiated in the career data domain, thus yielding Aviator, a collaborative platform capable of producing complete, intuitive and relevant answers to career related queries, in a time effective manner. In addition to pro- The Good Careers Guide, London, UK viding numeric and use case based evidence to support these research claims, this extended work also contains a detailed architectural analysis of Aviator to outline its suitability for automatically curating knowledge to a high standard of quality.

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“…Analysing information value These steps adapt existing goal modelling [12] and statistical decision analysis techniques [15] to the particular context of sustainability decisions in large organisation. Our approach has been strongly influenced by our recent work on software requirements and architecture decisions under uncertainty [25].…”

Section: Sustainability Decisions Under Uncertaintymentioning

confidence: 99%

Supporting Sustainability Decisions in Large Organisations

Stefan¹,

Letier²

2014

Proceedings of the 2014 Conference ICT for Sustainability

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Abstract-Sustainability management systems are important technologies used by large organisations to monitor and control their CO2 emissions and other environmental impacts. So far, these systems have been designed to support data collection and reporting mandated by regulations, but they provide little support for decision making in order to improve an organisation's environmental sustainability. In this paper, we present a systematic method to help organisations make such decisions. Our method builds on goal-oriented requirements engineering for modelling an organisation's sustainability goals and on statistical decision analysis for guiding decisions under uncertainty. By being explicit about uncertainty, our method provides a sound assessment of the risks associated with alternative options (including the "business as usual" option), and, through the concept of information value, informs decision makers whether reducing uncertainty through additional data collection will be useful for their decisions. We illustrate our approach on real sustainability decision questions at UCL's Department of Computer Science.

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Uncertainty, risk, and information value in software requirements and architecture

Cited by 79 publications

References 67 publications

Exact Analysis for Next Release Problem

Exact Analysis for Next Release Problem

An architecture for the autonomic curation of crowdsourced knowledge

Supporting Sustainability Decisions in Large Organisations

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