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Record W2159965810 · doi:10.1109/fie.2002.1158710

Process activities in a project based course in software engineering

2003· article· en· W2159965810 on OpenAlexaffabout
T. Germain, P.N. Robillard, M. Dulipovici

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPersonal software processSoftware Engineering Process GroupSoftware engineeringSoftware developmentComputer scienceProcess (computing)Software development processTeam software processSoftware project managementSocial software engineeringEngineering managementSoftware constructionSoftwareEngineeringProgramming language

Abstract

fetched live from OpenAlex

"Studio in Software Engineering" is a curriculum component for the undergraduate-level software engineering program at Ecole Polytechnique de Montreal. The main teaching objective is to develop in students a professional attitude towards producing high quality software. The course is based on a project approach in a collaborative learning environment. The software development process used is based on the Unified Process for EDUcation, which is customized from the Rational Unified Process. An insight into the dynamics of three teams involved in the development of the same project allows us to present and interpret data concerning the effort spent by students during particular process activities. The contribution of this paper is to illustrate an approach involving qualitative analysis of the effort spent by the students on each software process activity. Such an approach may allow the development of a model that would lead to effort prediction within a software process in order to designate the actions for improving academic projects.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.015
GPT teacher head0.276
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2003
Admission routes2
Has abstractyes

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