MétaCan
Menu
Back to cohort
Record W2151949169 · doi:10.1109/iwpse.2005.8

Change Impact Analysis for Requirement Evolution using Use Case Maps

2006· article· en· W2151949169 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsChange impact analysisComputer scienceSoftware engineeringSlicingSoftware requirements specificationRequirements analysisProcess (computing)Software systemSoftwareDependency (UML)Software evolutionSystems analysisSystems engineeringSoftware constructionEngineeringProgramming language

Abstract

fetched live from OpenAlex

Changing customer needs and computer technology are the driving factors influencing software evolution. There is a need to assess the impact of these changes on existing software systems. Requirement specification is gaining increasingly attention as a critical phase of software systems development process. In particular for larger systems, it quickly becomes difficult to comprehend what impact a requirement change might have on the overall system or parts of the system. Thus, the development of techniques and tools to support the evolution of requirement specifications becomes an important issue. In this paper we present a novel approach to change impact analysis at the requirement level. We apply both slicing and dependency analysis at the use case map specification level to identify the potential impact of requirement changes on the overall system. We illustrate our approach and its applicability with a case study conducted on a simple telephony system.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.118
GPT teacher head0.349
Teacher spread0.231 · 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

Quick stats

Citations59
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicSoftware Engineering ResearchFrench-language works237,207