Bibliographic record
Abstract
With the rise in software ecosystem initiatives, developing Application Programming Interfaces (APIs) has become an increasingly common practice. One main concern in developing APIs is that they expose back-end systems and data towards clients. This exposure threatens critical non-functional requirements, such as security of the back-end systems, performance of the provided services, and privacy of the communications with clients. Although dealing with non-functional requirements during software design has been long studied, there is still no framework to specifically assist software developers with addressing these requirements in APIs. In this thesis, we introduce Rational API Designer (RAPID), an assistant that provides consultation about designing non-functional requirements in the architecture of APIs. We have equipped RAPID with a broad range of expert knowledge about API design, systematically collected and extracted from the literature. The API design knowledge has been encoded as a set of 156 catalogues using the Non-Functional Requirements (NFR) language, a multi-valued logic commonly used for describing non-functional and functional requirements of software systems. RAPID uses the API design catalogues in a step-wise refinement and selection procedure to arrive from a given requirement, to a set of design alternatives, to a final suggestion for a given API design specification. Seven well-experienced developers have blindly evaluated accuracy of RAPID’s consultations over seven different cases of API design and on providing design guidelines for thirty design questions. The results of evaluation show that RAPID’s recommendations meet acceptable standards of the majority of the evaluators 73.3% of the time. Moreover, analysis of evaluators’ comments suggests that more than one-third of the unacceptable ratings (34%) given to RAPID’s answers are due to valid but incomplete design guidelines. We thus expect that the accuracy of the consultations will increase as RAPID’s knowledge of API design is extended and refined. Software developers can use RAPID to receive design suggestions generated through reusing the knowledge of experts, or to add their own knowledge of API design to RAPID for future use of their peers.
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How this classification was reachedexpand
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".