Assessment of Contemporary Modularization Techniques - ACoM'07
Bibliographic record
Abstract
The effective assessment of emerging modularization technologies plays a pivotal role on: (i) a better understanding of their real benefits and drawbacks when compared to conventional development techniques, and (ii) their effective transfer to mainstream software development. This report is intended to summarize the results of the 1st International Workshop on Assessment of Contemporary Modularization Techniques (ACoM'07) held in Minneapolis, USA, May 22, 2007, as part of the 29th International Conference on Software Engineering (ICSE'07). The main purpose of this workshop was to share and pool the collective experience of people interested in and actively working on assessment of innovative modularization techniques. The workshop consisted of an opening presentation, several paper presentations organized into three technical sessions, and four discussion groups. During the workshop presentations and discussions, the authors and participants directly and indirectly reviewed ongoing and previous work and debated a number of important issues on contemporary modularity assessment. The ACoM'07 website, including the electronic version of this report, can be found at . We begin by presenting an overview of our goals and the workshop structure, and then focus on the workshop technical program and results.
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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 | 0.032 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".