Temporal analysis of API usage concepts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract—Software reuse through Application Programming Interfaces (APIs) is an integral part of software development. The functionality offered by an API is not always accessed uniformly throughout the lifetime of a client program. We propose Temporal API Usage Pattern Mining to detect API usage patterns in terms of their time of introduction into client programs. We detect concepts as distinct groups of API functionality from the change history of a client program. We locate those concepts in the client change history and detect temporal usage patterns, where a pattern contains a set of concepts that were added into the client program in a specific temporal order. We investigated the properties of temporal API usage patterns through a multiple-case study of three APIs and their use in up to 19 client software projects. Our technique was able to detect a number of valuable patterns in two out of three of the APIs investigated. Further investigation showed some patterns to be relatively consistent between clients, produced by multiple developers, and not trivially derivable from program structure or API documentation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it