Breakthroughs and Challenges in Software Engineering
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.031 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".