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Record W2137995052 · doi:10.1109/hicss.2005.561

Strategic Release Planning and Evaluation of Operational Feasibility

2005· article· en· W2137995052 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStrategic planningBottleneckStakeholderProcess managementComputer scienceOperational planningStrategic human resource planningStrategic managementStrategic alignmentStrategic financial managementManagement scienceOperations researchBusinessEngineeringManagement

Abstract

fetched live from OpenAlex

Strategic planning (or road-mapping) of software releases addresses the assignment of requirements to releases on a strategic level. Effort, finance and risk constraints are considered to determine strategic release plans. The goal is to find an optimal balance between competing stakeholder priorities and bottleneck resources. However, strategic planning has to be supplemented by more fine-grained operational planning as typically performed in project management. The paper describes mechanisms by which to reduce the complexity of strategic and operational planning to a series of data and formulae that objectively represent input from all stakeholders and can easily reported, analyzed and manipulated. The capability provides improved planning and re-planning in a dynamic business environment, including the ability to validate strategic plans against operational limitations and revise as necessary. For performing strategic planning, we present the research prototype ReleasePlanner™. Real-world experience in performing strategic planning using ReleasePlanner is reported from a case study at Trema Laboratories Inc.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.114

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.117
GPT teacher head0.367
Teacher spread0.250 · 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