Bibliographic record
Abstract
Although software developers typically have access to numerous refactoring tools, most developers avoid using these tools despite their benefits. Researchers have identified many reasons for the disuse of refactoring tools, including a lack of awareness by the developers, a lack of predictability of the tools, and a lack of need for the tools. In this paper, we build on this earlier work and employ the ISO 9241-11 definition of usability to develop a theory of usability for refactoring tools. We investigate existing refactoring tools using this theory by analyzing how 17 developers experience refactoring tools in three software change tasks we asked them to perform. We analyze qualitatively the resulting interview transcripts based on our theory and report on a number of observations that can inform tool designers interested in improving the usability of refactoring tools. For instance, we found a desire for developers to guide how a refactoring tool changes the code and a need for refactoring tools to describe changes made to developers. Refactoring tools are currently expected to preserve program behavior. These observations indicate that it may be necessary to give developers more control over this property, including the ability to relax it, for the tools to be usable; that is, for the tools to be effective, efficient and satisfying for the developer to employ.
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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 | 0.058 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".