SST'19 - Software and Systems Traceability
Bibliographic record
Abstract
Traceability is the ability to relate di erent artifacts during the development and operation of a system to each other. It enables program comprehension, change impact analysis, and facilitates the cooperation of engineers from di erent disciplines. The 10th International Workshop on Software and Systems Traceability (former International Workshop on Traceability in Emerging Forms of Software Engineering, TEFSE), explored the role and impact of traceability in modern software and systems development. The event brought together researchers and practitioners to examine the challenges of recovering, maintaining, and utilizing traceability for the myriad forms of software and systems engineering artifacts. SST'19 was a highly interactive working event focused on discussing the main problems related to software traceability in particular in the context of opportunities and challenges posed by the recent progress in Arti cial Intelligence techniques and proposing possible solutions for such problems.
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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.032 | 0.025 |
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".