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API Call - Astrophysics Data System

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Last Updated: 16 January 2022

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Predictive Synthesis of API-Centric Code

Program synthesizers can provide significant coding support to this group of users, however, program execution can be sluggish due to large search spaces. We explore how machine learning can be used to speed up enumerative program synthesis in this article. We can speed up an enumerative program synthesizer by using predictions from our model variants, which we demonstrate. These speeds significantly outperform previous methods in which researchers have used ML models in enumerative synthesis.

Source link: https://ui.adsabs.harvard.edu/abs/2022arXiv220103758N/abstract


OWLOOP: A modular API to describe OWL axioms in OOP objects hierarchies

Object-Oriented Programming is an Application Programming Interface for using the Ontology Web Language by the means of Object-Oriented Programming. OWLOOP is an Application Programming Interface for using the Ontology Web Language. If the components of an architecture also use OWL ontologies for knowledge representation and reasoning, they would need to be linked with OWL axioms. We're extending OWL-API to include a general-purpose interface between OWL axioms subject to reasoning and modular OOP objects hierarchies.

Source link: https://ui.adsabs.harvard.edu/abs/2022SoftX..1700952B/abstract


Lyncs-API: a Python API for Lattice QCD applications

Currently under production, we're happy to announce Lyncs-API, a Python API for Lattice QCD applications that is still under testing. Lyncs aims to bring many commonly used libraries for Lattice QCD under a single roof. Lyncs flexibly links to CPUs and GPUs in a way that will support additional computational architectures as these arise, providing performance-portability for the calculations while keeping the same high-level workflow. In addition, we have developed generic-purpose tools for facilitating the usage of Python in Lattice QCD and HPC in general.

Source link: https://ui.adsabs.harvard.edu/abs/2022arXiv220103873B/abstract


ARCLIN: Automated API Mention Resolution for Unformatted Texts

Developer forums are often used by developers to discuss technical issues such as how to use specific Application Programming Interfaces, how to code the applications, or how to fix bugs in their code. Identifying API names without a fully qualified name; identifying API names; and disambiguating API names in a different library are three major obstacles for accurate API recognition and linking mentioned APIs from unstructured natural language documents to an API repository: distinguishing API names from common words; identifying API mentions with similar method names; and identifying API references with similar method names; and disambiguating API references with similar method names; and referring API names to an entry in the API repository. We first develop an API recognizer to automatically extract API mentions from natural language sentences by a Condition Random Field on the top of a Bi-directional Long-Term Memory module's Bi-directional Long Short-Term Memory module, and then use a context-based scoring system to determine the mention-entry similarity for each entry in an API repository.

Source link: https://ui.adsabs.harvard.edu/abs/2022arXiv220101459H/abstract


Applications of Eichhornia crapies leaf extract as an green inhibitor of steel API SL grade B media HCL 1M solution

Corrosion is a natural process that converts carbon steel into a more chemically stable form, such as oxide. In general, rust is unavoidable, but the corrosion rate can be slowed down. Corrosion is the gradual degradation of metals on metals by chemical or electrochemical reactions with its environment. Various ways are used to reduce the corrosion damage caused by metal objects exposed to weather, brine, acids, or the atmosphere. But in practice, however, environmentally friendly materials were used as corrosion prevention material that was also harmful to the climate. In a 1 million acid HCL Solution, it is intended to determine the effect of eichhornia crasipes extract as a environment friendly inhibitor of API 5L Grade B Steel. The results of this research indicated that the inhibitors of organic Eichornia crasipes extract could effectively reduce the corrosion rate of API 5L Grade B steel in acid circles.

Source link: https://ui.adsabs.harvard.edu/abs/2021AIPC.2384g0003P/abstract


Towards a secure API client generator for IoT devices

However, the support for networking functions is not appropriate in the case of single board microcontrollers, and different IoT platforms enable access to the networking submodule through various libraries and system calls, contributing to a steeper learning curve. API client code generators can help with code optimization, but they tend to produce universal purpose code, and on the other hand, IoT device networking primitives are platform specific, particularly when security mechanisms such as Transport Layer Security are included. This paper discusses extttcpp-client, an API client generator developed as a plug for the OpenAPI Generator project, which can tailor the generated code based on the user's IoT platform. Our work helps to produce correct code for API clients for IoT devices, boosting a programmer's efficiency and a quicker time-to-market for its own applications.

Source link: https://ui.adsabs.harvard.edu/abs/2022arXiv220100270A/abstract


REST API Fuzzing by Coverage Level Guided Blackbox Testing

REST APIs have become the most common communication channel between services, with the rise of web applications. Black box fuzz testing is one of the most popular techniques to perform experiments on a large scale. However, conventional black box fuzz testing provides random results without judging the input's quality. For REST APIs, we use a black box fuzz testing scheme. We also improved mutation tactics by lowering the testing complexity for REST APIs, as well as providing more test cases to describe potential paths. In addition, we find 351 bugs from 64 remote API services in APIs. guru.

Source link: https://ui.adsabs.harvard.edu/abs/2021arXiv211215485T/abstract


HAPI: An API Standard for Accessing Heliophysics Time Series Data

Although there are now only a few commonly used data file formats, the variety in software for automated access to and aggregation of such data files can make analysis that necessitates intercomparison of data from several data providers difficult. The Heliophysics Application Programmer's Interface is a newly developed framework for accessing distributed time series data to increase interoperability. The HAPI specification is based on existing data services' common elements, and it standardizes the two main components of a data service: the request interface and response data structures. Multiple mature HAPI-related open-source projects include server-side configuration tools and client-side libraries for viewing HAPI data in several languages. HAPI connectivity has been added to several data providers in the United States and Europe's latest interfaces. HAPI has been developed as a COSPAR initiative for time series data delivery, and it has been added as a COSPAR component for time series data delivery.

Source link: https://ui.adsabs.harvard.edu/abs/2021JGRA..12629534W/abstract


Revisiting, Benchmarking and Exploring API Recommendation: How Far Are We?

Application Programming Interfaces (AP), which encapsulate the development of specific applications as interfaces, can greatly improve the effectiveness of modern software development's productivity. As the number of APIs rises today, developers can hardly be familiar with all the APIs, and developers can often look for appropriate APIs for use. A huge number of initiatives have been devoted to upgrading the API recommendation process. For example, some studies see the task as a code completion issue, while others recommend relative APIs for natural language queries. In this paper, we revisit the API recommendation task and aim at benchmarking the approaches in order to minimize the risks and improve future research. Specifically, the paper divides the approaches into two categories based on the task's scope, i. e. , query-based API recommendation and code-based API recommendation. APIBench is then developed for the two specific types of approaches.

Source link: https://ui.adsabs.harvard.edu/abs/2021arXiv211212653P/abstract

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions