The Frog and the Octopus—Experience Teaching Software Project Management
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".