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"Application programming interfaces"
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ProSy: API-Based Synthesis with Probabilistic Model
Program synthesis is an exciting topic that desires to generate programs satisfying user intent automatically. But in most cases, only small programs for simple or domain-specific tasks can be synthesized. The major obstacle of synthesis lies in the huge search space. A common practice in addressing this problem is using a domain-specific language, while many approaches still wish to synthesize programs in general programming languages. With the rapid growth of reusable libraries, component-based synthesis provides a promising way, such as synthesizing Java programs which are only composed of APIs (application programming interfaces). However, the efficiency of searching for proper solutions for complex tasks is still a challenge. Given an unfamiliar programming task, programmers would search for API usage knowledge from various coding resources to reduce the search space. Considering this, we propose a novel approach named ProSy to synthesize API-based programs in Java. The key novelty is to retrieve related knowledge from Javadoc and Stack Overflow and then construct a probabilistic reachability graph. It assigns higher probabilities to APIs that are more likely to be used in implementing the given task. In the synthesis process, the program sketch with a higher probability will be considered first; thus, the number of explored reachable paths would be decreased. Some extension and optimization strategies are further studied in the paper. We implement our approach and conduct several experiments on it. We compare ProSy with SyPet and other state-of-the-art API-based synthesis approaches. The experimental results show that ProSy reduces the synthesis time of SyPet by up to 80%.
Journal Article
Programming the Intel Galileo : getting started with the Arduino-compatible development board
Get started programming your own fun electronics projects-no experience required! This hands-on guide offers a clear introduction to programming the Intel Galileo using Arduino Software. The book explains Intel Galileo hardware fundamentals and shows, step-by-step, how to write well-crafted sketches using easy-to-follow examples that move from basic to more advanced programming concepts. Programming the Intel Galileo: Getting Started with the Arduino-Compatible Development Board shows how to set up, connect, and quickly start programming the Intel Galileo. You will discover how to work with the board's inputs and outputs, use libraries, and connect to the Internet. From there, you will learn to program your own Galileo-based creations using Arduino's modified C language. * Serves both as a makers' guide and as an introduction for techs, developers, and engineers * Features a series of hands-on projects along with screenshots, diagrams, and source code * Written by a dedicated hobbyist and experienced author.
Book
Recommending reference API documentation
Reference documentation is an important source of information on API usage. However, information useful to programmers can be buried in irrelevant text, or attached to a non-intuitive API element, making it difficult to discover. We propose to detect and recommend fragments of API documentation potentially important to a programmer who has already decided to use a certain API element. We categorize text fragments in API documentation based on whether they contain information that is
indispensable
,
valuable
, or neither. From the fragments that contain knowledge worthy of recommendation, we extract word patterns, and use these patterns to automatically find new fragments that contain similar knowledge in unseen documentation. We implemented our technique in a tool, Krec, that supports both information filtering and discovery. In an evaluation study with randomly-sampled method definitions from ten open source systems, we found that with a training set derived from about 1000 documentation units, we could issue recommendations with 90 % precision and 69 % recall. In a study involving ten independent assessors, indispensable knowledge items recommended for API types were judged useful 57 % of the time and potentially useful an additional 30 % of the time.
Journal Article
Objective-C for absolute beginners : iPhone, iPad and Mac programming made easy
\"Learn Objective-C and its latest release, and learn how to mix Swift with it. You have a great idea for an app, but how do you bring it to fruition? With Objective-C, the universal language of iPhone, iPad, and Mac apps. Using a hands-on approach, you'll learn how to think in programming terms, how to use Objective-C to construct program logic, and how to synthesize it all into working apps. Gary Bennett, an experienced app developer and trainer, will guide you on your journey to becoming a successful app developer. Along the way you'll discover the flexibility of Apple's developer tools. If you're looking to take the first step towards App Store success, Objective-C for Absolute Beginners, Fourth Edition is the place to start\"--Page [4]
Book
The Impact of AI-Driven Application Programming Interfaces (APIs) on Educational Information Management
In today’s digitalized educational landscape, the intelligent use of information is essential for personalizing learning, improving assessment accuracy, and supporting data-driven pedagogical decisions. This systematic review examines the integration of Application Programming Interfaces (APIs) powered by Artificial Intelligence (AI) to enhance educational information management and learning processes. A total of 27 peer-reviewed studies published between 2013 and 2025 were analyzed. First, a general description of the selected works was provided, followed by a breakdown by dimensions in order to identify recurring patterns, stated interests and gaps in the current scientific literature on the use of AI-driven APIs in Education. The findings highlight five main benefits: data interoperability, personalized learning, automated feedback, real-time student monitoring, and predictive performance analytics. All studies addressed personalization, 74.1% focused on platform integration, and 37% examined automated feedback. Reported outcomes include improvements in engagement (63%), comprehension (55.6%), and academic achievement (48.1%). However, the review also identifies concerns about privacy, algorithmic bias, and limited methodological rigor in existing research. The study concludes with a conceptual model that synthesizes these findings from pedagogical, technological, and ethical perspectives, providing guidance for more adaptive, inclusive, and responsible uses of AI in education.
Journal Article
Beyond One-Off Integrations: A Commercial, Substitutable, Reusable, Standards-Based, Electronic Health Record–Connected App
The Substitutable Medical Apps and Reusable Technology (SMART) Health IT project launched in 2010 to facilitate the development of medical apps that are scalable and substitutable. SMART defines an open application programming interface (API) specification that enables apps to connect to electronic health record systems and data warehouses without custom integration efforts. The SMART-enabled version of the Meducation app, developed by Polyglot, has been implemented at scores of hospitals and clinics in the United States, nation-wide. After expanding their product's reach by relying on a universal, open API for integrations, the team estimates that one project manager can handle up to 20 simultaneous implementations. The app is made available through the SMART App Gallery, an open app store that supports discovery of apps and, because the apps are substitutable, market competition. This case illustrates how a universal open API for patient and clinician-facing health IT systems supported and accelerated commercial success for a start-up company. Giving end users a wide and ever-growing choice of apps that leverage data generated by the health care system and patients at home through a universal, open API is a promising and generalizable approach for rapid diffusion of innovation across health systems.
Journal Article
Harvesting ambient geospatial information from social media feeds
Social media generated from many individuals is playing a greater role in our daily lives and provides a unique opportunity to gain valuable insight on information flow and social networking within a society. Through data collection and analysis of its content, it supports a greater mapping and understanding of the evolving human landscape. The information disseminated through such media represents a deviation from volunteered geography, in the sense that it is not geographic information per se. Nevertheless, the message often has geographic footprints, for example, in the form of locations from where the tweets originate, or references in their content to geographic entities. We argue that such data conveys ambient geospatial information, capturing for example, people's references to locations that represent momentary social hotspots. In this paper we address a framework to harvest such ambient geospatial information, and resulting hybrid capabilities to analyze it to support situational awareness as it relates to human activities. We argue that this emergence of ambient geospatial analysis represents a second step in the evolution of geospatial data availability, following on the heels of volunteered geographical information.
Journal Article