The quest for Ubiquity: A roadmap for software and systems traceability research
Bibliographic record
Abstract
Traceability underlies many important software and systems engineering activities, such as change impact analysis and regression testing. Despite important research advances, as in the automated creation and maintenance of trace links, traceability implementation and use is still not pervasive in industry. A community of traceability researchers and practitioners has been collaborating to understand the hurdles to making traceability ubiquitous. Over a series of years, workshops have been held to elicit and enhance research challenges and related tasks to address these shortcomings. A continuing discussion of the community has resulted in the research roadmap of this paper. We present a brief view of the state of the art in traceability, the grand challenge for traceability and future directions for the field.
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 | 0.045 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.017 | 0.097 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 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".