Report on the 13th ACM/IEEE International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems - SESoS@ICSE 2025
Bibliographic record
Abstract
This article reports on the results of the 13th ACM/IEEE International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems (SESoS 2025) in which researchers and practitioners discussed ideas and experiences on the research and practice for the development and evolution of complex softwareintensive systems, more specifically systems-of-systems (SoS) and software ecosystems (SECO). SESoS 2025 was co-located with the 47th IEEE/ACM International Conference on Software Engineering (ICSE 2025). After over a decade running this workshop, the SESoS community is advancing on how to cope with the different dimensions that should be considered in the engineering of those classes of systems (i.e. technological, organizational, and social), and also is taking awareness of newer challenges for inclusiveness and sustainability. In addition, benchmarks for conducting research on the areas as well as approaches for investigating emerging domains (smart ecosystems) and non-functional requirements on those systems were pointed out as relevant challenges.
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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.152 | 0.063 |
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".