The effective execution of tests for REST APIs remains a considerable challenge for development teams, driven by the inherent complexity of distributed systems, the multitude of possible scenarios, and the limited time available for test design. Exhaustive testing of all input combinations is impractical, often resulting in undetected failures, high manual effort, and limited test coverage. To address these issues, we introduce RestTSLLM, an approach that uses Test Specification Language (TSL) in conjunction with Large Language Models (LLMs) to automate the generation of test cases for REST APIs. The approach targets two core challenges: the creation of test scenarios and the definition of appropriate input data. The proposed solution integrates prompt engineering techniques with an automated pipeline to evaluate various LLMs on their ability to generate tests from OpenAPI specifications. The evaluation focused on metrics such as success rate, test coverage, and mutation score, enabling a systematic comparison of model performance. The results indicate that the best-performing LLMs - Claude 3.5 Sonnet (Anthropic), Deepseek R1 (Deepseek), Qwen 2.5 32b (Alibaba), and Sabia 3 (Maritaca) - consistently produced robust and contextually coherent REST API tests. Among them, Claude 3.5 Sonnet outperformed all other models across every metric, emerging in this study as the most suitable model for this task. These findings highlight the potential of LLMs to automate the generation of tests based on API specifications.

This work elaborates on a High performance computing (HPC) architecture based on Simple Linux Utility for Resource Management (SLURM) [1] for deploying heterogeneous Large Language Models (LLMs) into a scalable inference engine. Dynamic resource scheduling and seamless integration of containerized microservices have been leveraged herein to manage CPU, GPU, and memory allocations efficiently in multi-node clusters. Extensive experiments, using Llama 3.2 (1B and 3B parameters) [2] and Llama 3.1 (8B and 70B) [3], probe throughput, latency, and concurrency and show that small models can handle up to 128 concurrent requests at sub-50 ms latency, while for larger models, saturation happens with as few as two concurrent users, with a latency of more than 2 seconds. This architecture includes Representational State Transfer Application Programming Interfaces (REST APIs) [4] endpoints for single and bulk inferences, as well as advanced workflows such as multi-step "tribunal" refinement. Experimental results confirm minimal overhead from container and scheduling activities and show that the approach scales reliably both for batch and interactive settings. We further illustrate real-world scenarios, including the deployment of chatbots with retrievalaugmented generation, which helps to demonstrate the flexibility and robustness of the architecture. The obtained results pave ways for significantly more efficient, responsive, and fault-tolerant LLM inference on large-scale HPC infrastructures.

The microservices architectural style has become the de facto standard for large-scale cloud applications, offering numerous benefits in scalability, maintainability, and deployment flexibility. Many organizations are pursuing the migration of legacy monolithic systems to a microservices architecture. However, this process is challenging, risky, time-intensive, and prone-to-failure while several organizations lack necessary financial resources, time, or expertise to set up this migration process. So, rather than trying to migrate a legacy system where migration is risky or not feasible, we suggest exposing it as a microservice application without without having to migrate it. In this paper, we present a reusable, automated, two-phase approach that combines evolutionary algorithms with machine learning techniques. In the first phase, we identify microservices at the method level using a multi-objective genetic algorithm that considers both structural and semantic dependencies between methods. In the second phase, we generate REST APIs for each identified microservice using a classification algorithm to assign HTTP methods and endpoints. We evaluated our approach with a case study on the Spring PetClinic application, which has both monolithic and microservices implementations that serve as ground truth for comparison. Results demonstrate that our approach successfully aligns identified microservices with those in the reference microservices implementation, highlighting its effectiveness in service identification and API generation.

REST APIs have become key components of web services. However, they often contain logic flaws resulting in server side errors or security vulnerabilities. HTTP requests are used as test cases to find and mitigate such issues. Existing methods to modify requests, including those using deep learning, suffer from limited performance and precision, relying on undirected search or making limited usage of the contextual information. In this paper we propose APIRL, a fully automated deep reinforcement learning tool for testing REST APIs. A key novelty of our approach is the use of feedback from a transformer module pre-trained on JSON-structured data, akin to that used in API responses. This allows APIRL to learn the subtleties relating to test outcomes, and generalise to unseen API endpoints. We show APIRL can find significantly more bugs than the state-of-the-art in real world REST APIs while minimising the number of required test cases. We also study how reward functions, and other key design choices, affect learnt policies in a thorough ablation study.

The rising popularity of the microservice architectural style has led to a growing demand for automated testing approaches tailored to these systems. EvoMaster is a state-of-the-art tool that uses Evolutionary Algorithms (EAs) to automatically generate test cases for microservices' REST APIs. One limitation of these EAs is the use of unit-level search heuristics, such as branch distances, which focus on fine-grained code coverage and may not effectively capture the complex, interconnected behaviors characteristic of system-level testing. To address this limitation, we propose a new search heuristic (MISH) that uses real-time automaton learning to guide the test case generation process. We capture the sequential call patterns exhibited by a test case by learning an automaton from the stream of log events outputted by different microservices within the same system. Therefore, MISH learns a representation of the systemwide behavior, allowing us to define the fitness of a test case based on the path it traverses within the inferred automaton. We empirically evaluate MISH's effectiveness on six real-world benchmark microservice applications and compare it against a state-of-the-art technique, MOSA, for testing REST APIs. Our evaluation shows promising results for using MISH to guide the automated test case generation within EvoMaster.

Research towards energy optimization in buildings heavily relies on building-related data such as measured indoor climate factors. While data collection is a labor- and cost-intensive task, simulations are a cheap alternative to generate datasets of arbitrary sizes, particularly useful for data-intensive deep learning methods. In this paper, we present the tool EnergyPlus Room Simulator, which enables the simulation of indoor climate in a specific room of a building using the simulation software EnergyPlus. It allows to alter room models and simulate various factors such as temperature, humidity, and CO2 concentration. In contrast to manually working with EnergyPlus, this tool enhances the simulation process by offering a convenient interface, including a user-friendly graphical user interface (GUI) as well as a REST API. The tool is intended to support scientific, building-related tasks such as occupancy detection on a room level by facilitating fast access to simulation data that may, for instance, be used for pre-training machine learning models.

They enable efficient management of resources and network traffic, a definite advantage in the age of increasingly complex networks requiring dynamic management. By centralizing control and enabling flexible management, SDN offers new opportunities for network optimization. Nevertheless, fully realizing the potential of SDN requires the development of advanced and adaptive control methods. This article focuses on analyzing current methods of flexible control for SDN networks and presenting a solution to improve the efficiency and adaptability of network management. The approach presented is based on the application of machine learning, specifically the Reinforcement Learning (RL) [2]. This technique allows networks to make autonomous decisions based on previous experiences and dynamically changing conditions, which is similar to the way humans learn. The goal of the proposed solution is to not only increase network performance, but to improve its flexibility and real-time adaptability. The use of reinforcement learning enables dynamic and flexible control of network traffic, resulting in more efficient and responsive resource management [3]. The article reviews existing solutions and describes in detail the original approach developed in-its own, pointing out its potential benefits and implementation possibilities.

Welcome to: Learn Python from Zero to Hero [Basic, GUI, Web, Full Stack as you know Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Python developers are in demand. Across a wide range of fields, there is a demand for those with Python skills. If you're looking to start or change your career, it could be a vital skill to help you. It could lead to a well-paid career. There will be many job opportunities.

Real-estate image tagging is one of the essential use-cases to save efforts involved in manual annotation and enhance the user experience. This paper proposes an end-to-end pipeline (referred to as RE-Tagger) for the real-estate image classification problem. We present a two-stage transfer learning approach using custom InceptionV3 architecture to classify images into different categories (i.e., bedroom, bathroom, kitchen, balcony, hall, and others). Finally, we released the application as REST API hosted as a web application running on 2 cores machine with 2 GB RAM. The demo video is available here.

Developers have discovered plenty of reasons to create microservices in Python, from its foundation in object-oriented programming, to its ability to handle REST APIs, to its out-of-the-box support for application prototyping. In particular, its proponents praise Python's array of built-in features that help isolate key application processes and integrate dynamic collections of distributed services. As is the case with any programming language, however, Python also introduces its share of challenges to navigate. For some -- particularly those not well-versed in interpreted languages or have pressing needs for quick compile times -- Python might not be the ideal language for their microservices development efforts. Let's look at the reasons why developers might want to create microservices in Python, examine the standout features that streamline application build processes, and point out the potential hurdles that developers may encounter.