Antonio Ruiz–Cortés

Fernando Ropero, Erkin Turkoz, Daniel Matos et al.

Visual Language Models (VLMs) have increasingly become the main paradigm for understanding indoor scenes, but they still struggle with metric and spatial reasoning. Current approaches rely on end-to-end video understanding or large-scale spatial question answering fine-tuning, inherently coupling perception and reasoning. In this paper, we investigate whether decoupling perception and reasoning leads to improved spatial reasoning. We propose an agentic framework for static 3D indoor scene reasoning that grounds an LLM in an explicit 3D scene graph (3DSG). Rather than ingesting videos directly, each scene is represented as a persistent 3DSG constructed by a dedicated perception module. To isolate reasoning performance, we instantiate the 3DSG from ground-truth annotations. The agent interacts with the scene exclusively through structured geometric tools that expose fundamental properties such as object dimensions, distances, poses, and spatial relationships. The results we obtain on the static split of VSI-Bench provide an upper bound under ideal perceptual conditions on the spatial reasoning performance, and we find that it is significantly higher than previous works, by up to 16\%, without task specific fine-tuning. Compared to base VLMs, our agentic variant achieves significantly better performance, with average improvements between 33\% to 50\%. These findings indicate that explicit geometric grounding substantially improves spatial reasoning performance, and suggest that structured representations offer a compelling alternative to purely end-to-end visual reasoning.

Cesar Garcia, Alejandro Guerrero, Joshua Zeitsoff et al.

Agile software teams are expected to follow a number of specific Team Practices (TPs) during each iteration, such as estimating the effort ("points") required to complete user stories and coordinating the management of the codebase with the delivery of features. For software engineering instructors trying to teach such TPs to student teams, manually auditing teams if teams are following the TPs and improving over time is tedious, time-consuming and error-prone. It is even more difficult when those TPs involve two or more tools. For example, starting work on a feature in a project-management tool such as Pivotal Tracker should usually be followed relatively quickly by the creation of a feature branch on GitHub. Merging a feature branch on GitHub should usually be followed relatively quickly by deploying the new feature to a staging server for customer feedback. Few systems are designed specifically to audit such TPs, and existing ones, as far as we know, are limited to a single specific tool. We present Bluejay, an open-source extensible platform that uses the APIs of multiple tools to collect raw data, synthesize it into TP measurements, and present dashboards to audit the TPs. A key insight in Bluejay's design is that TPs can be expressed in terminology similar to that used for modeling and auditing Service Level Agreement (SLA) compliance. Bluejay therefore builds on mature tools used in that ecosystem and adapts them for describing, auditing, and reporting on TPs. Bluejay currently consumes data from five different widely-used development tools, and can be customized by connecting it to any service with a REST API. Video showcase available at governify.io/showcase/bluejay

Francisco Javier Cavero, Juan C. Alonso, Antonio Ruiz-Cortés

The SaaS paradigm has revolutionized software distribution by offering flexible pricing options to meet diverse customer needs. However, the rapid expansion of the SaaS market has introduced significant complexity for DevOps teams, who must manually manage and evolve pricing structures, an approach that is both time-consuming and prone to errors. The absence of automated tools for pricing analysis restricts the ability to efficiently evaluate, optimize, and scale these models. This paper proposes leveraging intelligent pricing (iPricing), dynamic, machine-readable pricing models, as a solution to these challenges. Intelligent pricing enables competitive analysis, streamlines operational decision-making, and supports continuous pricing evolution in response to market dynamics, leading to improved efficiency and accuracy. We present an LLM-driven approach that automates the transformation of static HTML pricing into iPricing, significantly improving efficiency and consistency while minimizing human error. Our implementation, AI4Pricing2Yaml, features a basic Information Extractor that uses web scraping and LLMs technologies to extract essential pricing components, plans, features, usage limits, and add-ons, from SaaS websites. Validation against a dataset of 30 distinct commercial SaaS, encompassing over 150 intelligent pricings, demonstrates the system's effectiveness in extracting the desired elements across all steps. However, challenges remain in addressing hallucinations, complex structures, and dynamic content. This work highlights the potential of automating intelligent pricing transformation to streamline SaaS pricing management, offering implications for improved consistency and scalability in an increasingly intricate pricing landscape. Future research will focus on refining extraction capabilities and enhancing the system's adaptability to a wider range of SaaS websites.

Alberto Martin-Lopez, Sergio Segura, Carlos Müller et al.

Web services often impose inter-parameter dependencies that restrict the way in which two or more input parameters can be combined to form valid calls to the service. Unfortunately, current specification languages for web services like the OpenAPI Specification (OAS) provide no support for the formal description of such dependencies, which makes it hardly possible to automatically discover and interact with services without human intervention. In this article, we present an approach for the specification and automated analysis of inter-parameter dependencies in web APIs. We first present a domain-specific language, called Inter-parameter Dependency Language (IDL), for the specification of dependencies among input parameters in web services. Then, we propose a mapping to translate an IDL document into a constraint satisfaction problem (CSP), enabling the automated analysis of IDL specifications using standard CSP-based reasoning operations. Specifically, we present a catalogue of nine analysis operations on IDL documents allowing to compute, for example, whether a given request satisfies all the dependencies of the service. Finally, we present a tool suite including an editor, a parser, an OAS extension, a constraint programming-aided library, and a test suite supporting IDL specifications and their analyses. Together, these contributions pave the way for a new range of specification-driven applications in areas such as code generation and testing.

Javier Troya, Sergio Segura, Antonio Ruiz-Cortés

Model transformations are the cornerstone of Model-Driven Engineering, and provide the essential mechanisms for manipulating and transforming models. Checking whether the output of a model transformation is correct is a manual and error-prone task, this is referred to as the oracle problem in the software testing literature. The correctness of the model transformation program is crucial for the proper generation of its output, so it should be tested. Metamorphic testing is a testing technique to alleviate the oracle problem consisting on exploiting the relations between different inputs and outputs of the program under test, so-called metamorphic relations. In this paper we give an insight into our approach to generically define metamorphic relations for model transformations, which can be automatically instantiated given any specific model transformation.