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Record W2897620662 · doi:10.1109/re.2018.00038

Tailoring Requirements Negotiation to Sustainability

2018· article· en· W2897620662 on OpenAlexafffund
Norbert Seyff, Stefanie Betz, Letícia Duboc, Colin C. Venters, Christoph Becker, Ruzanna Chitchyan, Birgit Penzenstadler, Markus Nöbauer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaAgència per a la Competitivitat de l’EmpresaGeneralitat de CatalunyaEuropean Commission
KeywordsSustainabilityNegotiationLeverage (statistics)Process managementComputer scienceSustainability organizationsKey (lock)Social sustainabilityKnowledge managementRisk analysis (engineering)Management scienceBusinessEngineeringComputer securityPolitical science

Abstract

fetched live from OpenAlex

Requirements Engineering (RE) plays a critical role in software system development and is argued to be the key leverage point for practitioners who want to design sustainable software-intensive systems. However, existing RE methods and tools do not explicitly facilitate the discussion and negotiation of sustainability-related concerns. This leads to insufficient or onedimensional perceptions of sustainability. In this paper, we discuss our understanding of sustainability and its relationship with requirements. Based on the outcomes of this discussion, we have extended the WinWin Negotiation Model by incorporating sustainability concepts so that the negotiation also includes the ability to consider the impact of requirements on sustainability. Applying this negotiation method in an exploratory industrial case study, we have learned that this approach stimulates the discussion on sustainability and its multiple dimensions. It also allows practitioners to reflect on requirements and their effects on sustainability. However, we have also observed that further in-depth requirements analysis is needed to analyse the long-term effects of requirements regarding sustainability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.260
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2018
Admission routes2
Has abstractyes

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