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Record W2153764456 · doi:10.24908/pceea.v0i0.3610

The Frog and the Octopus—Experience Teaching Software Project Management

2011· article· en· W2153764456 on OpenAlexaffvenueabout
Philippe Kruchten

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of British ColumbiaNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsAgile software developmentComputer scienceProject managementSoftwareSet (abstract data type)Engineering managementSoftware engineeringEngineeringSystems engineering

Abstract

fetched live from OpenAlex

How do you teach software project management to 4th year engineering students, when there is nothing to manage, and the largest project they’ve ever experienced was with two buddies last term? In this paper, we present our experience over seven years teaching this topic alternatively to industrial practitioners, and to undergraduate and graduate students in an academic environment, both in Canada and in the Netherlands. The approach is based on a conceptual model of software development that takes into account the common aspects across a vast spectrum of software projects (“the frog”): intent, product, work, people, time, uncertainty, quality cost and value, and the variability across this spectrum (“the octopus”): size, criticality, business model, governance, team distribution, culture, etc.. This conceptual model is used throughout to (1) structure the course, (2) introduce issues, techniques, practices, and analyze them from a critical perspective: what would the frog say? what would the octopus say? (3) map other models, frameworks, or standards in this field: PMBOK, ISO 12207, RUP, Agile and lean approaches, ACM/IEEE SE 2004 curriculum. Rather than delivering to the students a canned set of recipes, the objective is to allow them to reason about the strategies, techniques, practices and tools that are most applicable to a given set of circumstances. The approach is complemented by small simulation games used to illustrate a few aspects and to trigger discussion (what happened, how realistic is this, how would you do differently?), or short videos of practices used to initiate a debate in class on a given practice.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.220
Teacher spread0.211 · 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 designTheoretical or conceptual
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

Citations2
Published2011
Admission routes3
Has abstractyes

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