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Record W6980287143

Breakthroughs and Challenges in Software Engineering

2023· article· en· W6980287143 on OpenAlexaboutno aff

Bibliographic record

VenueidUS (Universidad de Sevilla) · 2023
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSoftwareSoftware developmentWork (physics)Software system
DOInot available

Abstract

fetched live from OpenAlex

In the seminal 1968 NATO Conference on Software Engineering, F.L. Bauer defined this term as "the establishment and use of sound engineering principles in order to obtain software that is reliable and works efficiently on real machines".In the beginning, most proposals focused on programming languages and techniques, which are currently in a rather elaborate state.Later, the discipline experienced a shift from programming to project management, requirements, analysis, design, or maintenance issues.Furthermore, the Internet seems to be a new cornerstone that is gaining currency and paving the way for a new generation of software applications that are becoming more complex at an ever increasing pace.One of the reasons lies in the heterogeneous nature of the run-time and development-time aspects to be taken into account: attractiveness, quality of service, security, robustness, distribution, standards, and so on.Therefore, Software Engineering needs to keep producing methods and tools to tackle the increasing complexity of next-generation applications, and there is still place for research on methods and tools for project management, requirements elicitation, software analysis and design, testing and verification, or new supporting technologies.The goal of this special issue was to collect high-quality articles on Software Engineering breakthroughs and challenges.Our first attempt focused on the Spanish Conference on Software Engineering and Databases, which is a mature local conference in which researchers from Spain and surrounding countries meet yearly to share experiences and report on their latest findings.However, we thought that the issue would benefit from contributions around the world, and we decided to invite a number of renowned authors.All in all, we selected eight papers out of sixty five excellent submissions from Australia (5%), Canada (3%), Spain (60%), France (3%), Germany (3%), India (1%), Italy (4%), Japan (1%), Portugal (7%) Korea (4%), Singapore (2%), the United Kingdom (3%), and The United States of America (4%).The selection process was difficult since most articles seemed to be quite good.Only thirty articles passed the first reviewing round, of which we could select for publication only eight, namely:

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score0.557

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 head0.034
GPT teacher head0.248
Teacher spread0.215 · 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 designOther design
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

Citations0
Published2023
Admission routes1
Has abstractyes

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