The emergent structure of development tasks
Bibliographic record
Abstract
Abstract. Integrated development environments have been designed and engineered to display structural information about the source code of large systems. When a development task lines up with the structure of the system, the tools in these environments do a great job of supporting developers in their work. Unfortunately, many development tasks do not have this characteristic. Instead, they involve changes that are scattered across the source code and various other kinds of artifacts, including bug reports and documentation. Today’s development environments provide little support for working with scattered pieces of a system, and as a result, are not adequately supporting the ways in which developers work on the system. Fortunately, many development tasks do have a structure. This structure emerges from a developer’s actions when changing the system. In this paper, we describe how the structure of many tasks crosscuts system artifacts, and how by capturing that structure, we can make it as easy for developers to work on changes scattered across the system’s structure as it is to work on changes that line up with the system’s structure. 1
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".