AOSD and Reflection: Benefits and Drawbacks to Software Evolution Report on the WS RAM-SE at ECOOP'06
Bibliographic record
Abstract
Following last two years' RAM-SE (Reflection, AOP and Meta-Data for Software Evolution) workshop at the ECOOP conference, the RAM-SE 2006 workshop was a successful and popular event. As its name implies, the work- shop's focus was on the application of reflective, aspect-oriented and data-mining techniques to the broad field of software evolution. Topics and discussions at the workshop included mechanisms for supporting software evolution, technological limits for software evolution and tools and middleware for software evolution. The workshop's main goal was to bring together researchers working in the field of software evolution with a particular interest in reflection, aspect-oriented programming and meta-data. The workshop was organized as a full day meeting, partly devoted to presentation of submitted position papers and partly devoted to panel discussions about the presented topics and other interesting issues in the field. In this way, the workshop allowed participants to get acquainted with each other's work, and stimulated collaboration. We hope this helped participants in improving their ideas and the quality of their future publications. The workshop's proceedings, including all accepted position papers can be downloaded from the workshop's web site and a post workshop proceeding, in- cluding an extension of the accepted paper is published byt the University of Magdeburg. In this report, we first provide a session-by-session overview of the presenta- tions, and then present our opinions about future trends in software evolution.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".