Jabber Bot Remoted by REST API

Tato bakalářská práce popisuje implementaci a využití bota ovládaného přes REST API a komunikujícího prostřednictvím XMPP protokolu. Aplikace je navržena s ohledem na minimální náročnost na systémové prostředky, možnosti rozšíření a škálování, čímž je zajištěna možnost napojení na neomezený počet uživatelů. Práce rovněž obsahuje stručný popis vlastností XMPP protokolu, který je základním stavebním kamenem celé aplikace. V dalších kapitolách je popsána implementace webové aplikace demonstrující chování bota a také možnosti rozšíření.This bachelor’s thesis describes the implementation and use of Jabber bot controlled via REST API. The application is designed to meet the minimal demands on system resources, extensibility and scalability thus providing the ability to accept connections from an unlimited number of users. The work also contains a brief characterization of XMPP protocol, which is the cornerstone of the entire application. In subsequent chapters is described the implementation of a web application demonstrating the bot behavior and also the possibility of further extensions.

Implementasi Perbandingan Metode Graphql Dan Rest Api Pada Teknologi Nodejs

Penelitian ini bertujuan untuk membandingkan implementasi metode GraphQL dan REST API pada teknologi Node.js. GraphQL adalah bahasa query yang fleksibel dan efisien, sedangkan REST API telah menjadi standar de facto dalam pengembangan aplikasi web. Dalam penelitian ini, kami melakukan analisis mendalam terhadap kedua metode dengan membandingkan kinerja, kegunaan, dan pengalaman pengembang. Metode yang digunakan dalam penelitian ini adalah eksperimen, di mana kami mengembangkan dua aplikasi Node.js yang menerapkan metode GraphQL dan REST API secara terpisah. mengukur kinerja kedua metode menggunakan metrik seperti waktu respon  dan skalabilitas.Hasil penelitian menunjukkan bahwa kedua metode memiliki kelebihan dan kelemahan yang berbeda. GraphQL menawarkan fleksibilitas yang lebih tinggi dalam mengambil data dengan hanya meminta bagian yang diperlukan, mengurangi pengulangan dan mengoptimalkan penggunaan sumber daya. Namun, penggunaan GraphQL membutuhkan pembelajaran yang lebih mendalam dan pemahaman yang lebih baik tentang struktur data.   Kata Kunci : Perbandingan, GraphQL, REST API, Teknologi Node.js, Fleksibilitas

Implementing graphql in existing REST api

Classpip is the application developed by the student and professor of EETAC . The concept of classpip is based the principle of gamification which uses the gaming principle to the non-gaming context. Classpip applies the concept of gamification into education systems. The whole project of class pip is divided into 3 main parts: classpip dashboard which is the web application for desktop, classpip mobile is for the android /IOS devices and the classpip service which serves as the backend for these two clients. The main objective of this project is to improve the data fetching process by implementing Graphql. The whole work is done to implement graphql on top of existing rest api which uses a framework call loopback .We have tried various graphql architecture which can help improve the overall process of data fetching from the database. Graphql was developed by Facebook whose main goal is to increase the performance of the mobile users because in Graphql we only get what we need in just one request which saves the bandwidth and the time to load that data. Popular software companies like GitHub, Instagram, Twitter, Stack Share and more has already implemented graphql

PENERAPAN TEKNOLOGI QUICK RESPONSE CODE DAN APPLICATION PROGRAMMING INTERFACE PADA PERANCANGAN APLIKASI PERPUSTAKAAN (STUDI KASUS : SMP NEGERI 25 SURAKARTA)

SMP Negeri 25 Surakarta memiliki perpustakaan dengan banyak koleksi bahan pustaka tetapi untuk mengelola data masih menggunakan cara manual. Cara manual memiliki beberapa kekurangan antara lain lambat dalam proses pengolahan data, menimbulkan kesulitan ketika mencari data atau informasi, dan membutuhkan banyak ruang untuk menyimpan data. Oleh karena itu, penulis merancang aplikasi perpustakaan dengan menerapkan teknologi QR Code dan API di SMP Negeri 25 Surakarta. Aplikasi ini dikembangkan dengan metode Software Development Life Cycle (SDLC) model waterfall. Aplikasi perpustakaan ini dirancang berbasis website. Aplikasi ini dibuat menggunakan bahasa pemrograman PHP, framework codeigniter dan bootstrap, serta MySQL sebagai Database Management System (DBMS). Kemudian menambahkan ReST API server pada aplikasi tersebut agar data yang tersimpan dalam database dapat diakses untuk dikembangkan menjadi aplikasi android. Selanjutnya dibuat ReST API client dalam bentuk aplikasi android. ReST API client ini mengakses data milik ReST API server dan dikembangkan menjadi aplikasi baru. Hasil dari penelitian ini adalah aplikasi perpustakaan dengan menerapkan teknologi QR Code dan API di SMP Negeri 25 Surakarta

SplineAPI: A REST API for NLP Services

Modern applications often use Natural Language Processing (NLP) techniques and algorithms to provide sets of rich features. Researchers, who come up with these algorithms, often implement them for case studies, evaluation or as proof of concepts. These implementations are, in most cases, freely available for download and use. Nevertheless, these implementations do not comprise final software packages, with extensive installation instructions and detailed usage guides. Most lack a proper installation mechanism and library dependency tracking. The programming interfaces are, usually, limited to their usage through command line, or with just a few programming languages support. To overcome these shortcomings, this work aims to develop a new web platform to make available a set of common operations to third party applications that can be used to quickly access NLP based processes. Of course this platform still relies on the same tools mentioned before, as a base support to specific requests. Nevertheless, the end user will not need to install and learn their specific Application Programming Interfaces (API). For this to be possible, the architectural solution is to implement a RESTful API that hides all the tool details in a simple API that is common or, at least, coherent, across the different tools.FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/201

Методи та засоби тестування захищеності REST API. Фазінг REST API

Об'єктом дослідження є баги і вразливості в імплементації REST API веб- застосунків. Предметом дослідження є існуючі інструменти фазінгу REST API та підходи з їх використанням при пошуку багів REST API. Метою дослідження є порівняння існуючих підходів до фазінгу REST API та розробка власного методу тестування фазінгом API, проведення порівняльного аналізу застосування розробленого методу та існуючих інструментів на практиці.The object of the study is the bugs and vulnerabilities in the implementation of the REST API web applications. The subject of the research is the existing REST API fuzzing tools and approaches with their use in finding REST API bugs. The aim of the research is to compare the existing approaches to REST API fuzzing and to develop one's own testing method by API phasing, to conduct a comparative analysis of the application of the developed method and existing tools in practice

Adaptive REST API Testing with Reinforcement Learning

Modern web services increasingly rely on REST APIs. Effectively testing these APIs is challenging due to the vast search space to be explored, which involves selecting API operations for sequence creation, choosing parameters for each operation from a potentially large set of parameters, and sampling values from the virtually infinite parameter input space. Current testing tools lack efficient exploration mechanisms, treating all operations and parameters equally (i.e., not considering their importance or complexity) and lacking prioritization strategies. Furthermore, these tools struggle when response schemas are absent in the specification or exhibit variants. To address these limitations, we present an adaptive REST API testing technique that incorporates reinforcement learning to prioritize operations and parameters during exploration. Our approach dynamically analyzes request and response data to inform dependent parameters and adopts a sampling-based strategy for efficient processing of dynamic API feedback. We evaluated our technique on ten RESTful services, comparing it against state-of-the-art REST testing tools with respect to code coverage achieved, requests generated, operations covered, and service failures triggered. Additionally, we performed an ablation study on prioritization, dynamic feedback analysis, and sampling to assess their individual effects. Our findings demonstrate that our approach outperforms existing REST API testing tools in terms of effectiveness, efficiency, and fault-finding ability.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023