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Record W2004091746 · doi:10.1109/fie.2011.6142927

Providing end-to-end perspectives in software engineering

2011· article· en· W2004091746 on OpenAlexfundno aff
Michael Herold, Joe Bolinger, Rajiv Ramnath, Thomas E. Bihari, Jay Ramanathan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of CanadaNational Science Foundation
KeywordsComputer scienceCurriculumSoftware deploymentSoftware engineeringSoftware developmentContext (archaeology)Consistency (knowledge bases)Engineering managementSoftwareProcess (computing)Social software engineeringKnowledge managementEngineeringSoftware constructionPedagogyArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

In order to better prepare students for professional practice, we have created a software engineering curriculum that provides an end-to-end perspective that begins with the business context of software, and goes all the way to the ongoing management of software services after deployment. This paper examines how the theoretical aspects of this broad-based curriculum may be effectively delivered through a single course within a traditional computer science program. This curriculum is under a diverse set of constraints and requirements, such as the need for pedagogical consistency, faculty development, consideration of the learning style of computer science students, and a need for an effective continuous improvement process. Our approach uses “engineering-oriented” analysis frameworks such as Porter's Five Forces model for the business aspects, and attribute-driven design for software architectures, an “inverted” classroom mode of teaching where lectures are delivered on line with interactions and exercises that promote active learning reserved for the classroom, case studies developed from real projects to serve as concrete examples, open discussion boards and weekly short quizzes for concept refinement and retention, and a paper-based project where students apply the concepts learned. Faculty development and replication outside the current site are also discussed.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.732
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.249
Teacher spread0.213 · 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
GenreMethods

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

Citations4
Published2011
Admission routes1
Has abstractyes

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