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Record W7132911090

Assisting with API Design through Reusing Design Knowledge

2020· dissertation· W7132911090 on OpenAlexfundno aff
Mahsa Hasani Sadi

Bibliographic record

VenueTSpace · 2020
Typedissertation
Language
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsSoftware designApplication programming interfaceReuseSoftwareDesign knowledgeSet (abstract data type)Software requirements specificationDesign methods
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.261
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.229
GPT teacher head0.409
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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