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Record W2719617020 · doi:10.1109/chase.2017.6

Intertemporal Choice: Decision Making and Time in Software Engineering

2017· article· en· W2719617020 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNormativeManagement scienceComputer scienceEmpirical researchBusiness decision mappingIntersection (aeronautics)Intertemporal choiceSoftwareRisk analysis (engineering)Knowledge managementDecision support systemOperations researchEconomicsBusinessArtificial intelligenceEngineeringMicroeconomics

Abstract

fetched live from OpenAlex

When making choices in software projects, engineers and other stakeholders engage in decision making that involves uncertain future outcomes. The concept of 'intertemporal choice' describes choices between outcomes at different times in the future. Short-sighted decisions with far-reaching effects are a long-standing cause of concern in the software profession. Common models to support decisions in software projects use concepts such as expected utility and discount factors to quantify future value and enable trade-off decisions. However, a growing body of behavioral research shows that these normative models do not adequately describe how people actually make choices. Our objective is to understand how developers and stakeholders actually take trade-off decisions during software projects that involve current and future benefits, and to identify the human and cooperative factors that influence them. This requires empirical research on decision making in SE with a focus on trade-offs across time. To support such research, this paper reports on a systematic literature review that aimed to identify whether the intersection of these concepts has been acknowledged and addressed. We discuss the assumptions about decision makers that underpin existing research and analyze how the role of time has been characterized in the study of decision making in SE. Based on this review, the paper begins to develop principles for a descriptive framework to characterize intertemporal choices in empirical and behavioral software engineering research.

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.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.460
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.283
Teacher spread0.269 · 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

Quick stats

Citations12
Published2017
Admission routes2
Has abstractyes

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