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The Role of Use Cases in the UML

2002· book-chapter· en· W1500112013 on OpenAlexaff
Brian Dobing, Jeffrey Parsons

Bibliographic record

VenueIGI Global eBooks · 2002
Typebook-chapter
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsMemorial University of NewfoundlandUniversity of Lethbridge
Fundersnot available
KeywordsComputer scienceClass diagramClass (philosophy)Use Case DiagramVariety (cybernetics)Unified Modeling LanguageCategorizationAbstractionDatabase transactionSoftware engineeringProgramming languageSoftwareArtificial intelligenceEpistemology

Abstract

fetched live from OpenAlex

A use case is a description of a sequence of actions constituting a complete task or transaction in an application. Use cases were first proposed by Jacobson (1987) and have since been incorporated as one of the key modeling constructs in the UML (Booch, Jacobson, & Rumbaugh, 1999) and the Unified Software Development Process (Jacobson, Booch, & Rumbaugh, 1999). This chapter traces the development of use cases, and identifies a number of problems with both their application and theoretical underpinnings. From an application perspective, the use-case concept is marked by a high degree of variety in the level of abstraction versus implementation detail advocated by various authors. In addition, use cases are promoted as a primary mechanism for identifying objects in an application, even though they focus on processes rather than objects. Moreover, there is an apparent inconsistency between the so-called naturalness of object models and the commonly held view that use cases should be the primary means of communicating and verifying requirements with users. From a theoretical standpoint, the introduction of implementation issues in use cases can be seen as prematurely anchoring the analysis to particular implementation decisions. In addition, the fragmentation of objects across use cases creates conceptual difficulties in developing a comprehensive class diagram from a set of use cases. Moreover, the role of categorization in human thinking suggests that class diagrams may serve directly as a good mechanism for communicating and verifying application requirements with users. We conclude by outlining a framework for further empirical research to resolve issues raised in our analysis.

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: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.515

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 communication0.0000.000
Open science0.0020.000
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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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
GenreOther

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

Citations3
Published2002
Admission routes1
Has abstractyes

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