MétaCan
Menu
Back to cohort
Record W4286331388 · doi:10.1109/saner53432.2022.00046

What really is software design?

2022· article· en· W4286331388 on OpenAlexafffund
Giovanni Viviani, Gail C. Murphy

Bibliographic record

Venue2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · 2022
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSoftware designComputer scienceSoftware developmentSoftware engineeringSoftware constructionSocial software engineeringSoftware design descriptionSoftware peer reviewSoftwarePersonal software processContext (archaeology)Programming language

Abstract

fetched live from OpenAlex

Software design has been considered an integral part of software development for over fifty years. Over this time, software developers have improved how software systems are designed and have determined which designs lead to different desired characteristics in the systems built. In parallel, software engineering researchers have studied the processes software developers use to design and have considered many aspects of software design, such as how to represent a design. Given all of the practical experience gained and all of the study about software design, you might expect that there is a sophisticated common understanding about what software design is and is not. Unfortunately, such a common understanding is not evident in the literature. To investigate how software design is perceived, we conducted an interview study involving 16 participants representing both academia and industry. Our analysis of the interview transcripts reveals five main themes: 1) design cuts across multiple development phases and involves multiple people, 2) design involved decision making, 3) design is impacted by context, 4) design involves communication and 5) good design requires experience. We discuss the implications of these themes and describe what can be done to reach a more commonly shared idea of what design represents.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.290
Teacher spread0.251 · 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.

Study designSimulation or modeling
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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venue2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)Same topicSoftware Engineering Techniques and PracticesFrench-language works237,207