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Scientific peer review increasingly struggles to assess reproducibility at the scale and complexity of modern research output. Evaluating reproducibility requires reconstructing experimental dependencies, methodological choices, data flows,…
In industry, RESTful APIs are widely used to build modern Cloud Applications. Testing them is challenging, because not only they rely on network communications, but also they deal with external services like databases. Therefore, there has…
Even though numerous researchers require stable datasets along with source code and basic metrics calculated on them, neither GitHub nor any other code hosting platform provides such a resource. Consequently, each researcher must download…
Rateless Multiple Access (RMA) is a novel non-orthogonal multiple access framework that is promising for massive access in Internet of Things (IoT) due to its high efficiency and low complexity. In the framework, after certain…
Retrieval-augmented generation (RAG) systems are increasingly deployed in user-facing applications, yet systematic, human-centered evaluation of their outputs remains underexplored. Building on Gienapp's utility-dimension framework, we…
REST APIs are prevalent among web service implementations, easing interoperability through the HTTP protocol. API testers and users exploit the widely adopted OpenAPI Specification (OAS), a machine-readable standard to document REST APIs.…
The resurgence of autonomous agents built using large language models (LLMs) to solve complex real-world tasks has brought increased focus on LLMs' fundamental ability of tool or function calling. At the core of these agents, an LLM must…
Relational data stored in RDBMS is foundational to many real-world applications across domains such as e-commerce, finance, and sociality. While deep neural networks (DNNs) have achieved strong performance on tabular data with a single…
The performance model of an application can pro- vide understanding about its runtime behavior on particular hardware. Such information can be analyzed by developers for performance tuning. However, model building and analyzing is…
Recent developments in the world of services on the Web show that both the number of available Web APIs as well as the applications built on top is constantly increasing. This trend is commonly attributed to the wide adoption of the REST…
LLM agents are rapidly evolving from coding assistants into autonomous software engineering systems. However, existing evaluation methodologies remain largely centered on static, isolated, and short-horizon benchmarks that fail to capture…
Modern web services rely heavily on REST APIs, typically documented using the OpenAPI specification. The widespread adoption of this standard has resulted in the development of many black-box testing tools that generate tests based on…
For distributed applications to take full advantage of cloud computing systems, we need middleware systems that allow developers to build elasticity management components right into the applications. This paper describes the design and…
The widespread adoption of REST APIs, coupled with their growing complexity and size, has led to the need for automated REST API testing tools. Current tools focus on the structured data in REST API specifications but often neglect valuable…
The source code of Function as a Service (FaaS) applications is constantly being refined. To detect if a source code change introduces a significant performance regression, the traditional benchmarking approach evaluates both the old and…
It has been long debated that eXplainable AI (XAI) is an important topic, but it lacks rigorous definition and fair metrics. In this paper, we briefly summarize the status quo of the metrics, along with an exhaustive experimental study…
Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods either compromise on permutation equivariance or limit their…
Large language model (LLM) services have become an integral part of search, assistance, and decision-making applications. However, unlike traditional web or microservices, the hardware and software stack enabling LLM inference deployment is…
Nowadays, malware and malware incidents are increasing daily, even with various antivirus systems and malware detection or classification methodologies. Machine learning techniques have been the main focus of the security experts to detect…
In online learning from non-stationary data streams, it is necessary to learn robustly to outliers and to adapt quickly to changes in the underlying data generating mechanism. In this paper, we refer to the former attribute of online…