Abstract: The adoption of architectural patterns has recently been assessed in relation to their impact on the performance of microservice-based applications. For example, offloading common functionalities of multiple microservices to a gateway may improve the system response time. However, for a given performance requirement, such as service latency or resource utilization, the benefits of choosing an architectural pattern and its design decisions are not guaranteed. Therefore, it is crucial to collect data on patterns’ parameters, thereby providing insights to software architects on the relationships between design decisions and performance requirements. In this work, we propose a data-driven approach to assess the quantitative impact of a given pattern and its design decisions, thus understanding the achievement of performance tradeoffs. Our approach seeks to help architects understand and control the pattern parameters that cause variations, i. e. the sensitivity of performance tradeoffs, through scenario discovery techniques. To this end, we rely on machine learning algorithms (i. e., PRIM and CART) to infer constraints on the parameter values. The approach is embodied in a framework called ADEPT, which provides a workflow for generating a pattern design space along with the tradeoffs of interest, identifying key parameters, and then applying scenario discovery algorithms to these tradeoffs. We perform an extensive evaluation using a dataset collected from models of five microservice patterns and their performance characteristics. The experimental results show that ADEPT is effective in identifying key pattern parameters and inferring parameter constraints through PRIM and CART, thus understanding tradeoff variability. Thus, this research supports software architects in making informed design decisions by providing quantitative insights into the performance behavior of microservice patterns.

Abstract: Machine Translation (MT) is the backbone of multiple systems and applications leveraged everyday by users. Despite research efforts and progress in the MT domain, translation remains a challenging task and MT systems struggle when translating rare words, named entities, domain-specific terminology, idiomatic expressions and culturally specific terms. To meet the translation performance expectations of users, engineers periodically update (fine-tune) MT models to guarantee translation quality. However, with ever-growing machine learning models, fine-tuning operations become more expensive, raising serious sustainability concerns. Furthermore, not all fine-tunings guarantee increased translation quality, thus wasting compute resources.

To address this issue and enhance the sustainability of MT systems, we present Flexico, an approach to engineer self-adaptive MT systems, leveraging (i) ML-based regressors to estimate the expected benefits of fine-tuning MT models; and (ii) probabilistic model checking techniques to automate the reasoning about when the benefits of fine-tuning outweigh its costs. Our empirical evaluation on two MT models and language-pairs and across 9 domains demonstrates the predictive performance of the models that estimate the expected benefits of fine-tuning and their domain-generalizability. Flexico improves the sustainability of MT systems when compared to naive baselines and decreases the number of fine-tunings while preserving high translation quality.

Abstract: Deep Learning methods, particularly Convolutional Neural Networks (CNN), have become widely adopted in indus- trial computer vision applications due to their strong image classification capabilities. In industrial environments, reliable object recognition is essential for automated systems such as warehouse automation and sorting processes, where incorrect classifications may lead to operational failures and reduced system reliability. Despite their performance, CNN-based models remain vulnerable to adversarial perturbations, raising concerns regarding their deployment in safety-critical applications. Industrial vision systems deployed in safety-critical environ- ments must operate reliably under adversarial perturbations and common perception degradations. This work presents a robustness analysis of image classification models for industrial environments using RGB and RGB-D data. An industrial object dataset containing seven classes was constructed through an ob- ject detection and extraction pipeline, resulting in object-centered image samples. Two CNN-based architectures were developed and evaluated: a baseline RGB model and a multimodal RGB-D model. Model robustness was analyzed through both formal verifica- tion and corruption-based robustness evaluation. Formal verifi- cation was performed using the alpha-beta-CROWN framework under different perturbation magnitudes, assessing whether the CNNs preserve their predictions under bounded perturbations. In addition, corruption-based experiments following the ImageNet- C protocol were conducted to evaluate model behavior under common image degradations.

Abstract: The Robot Operating System (ROS) is the de facto framework for building open-source robotic software systems. Developers architect their system by using reusable components, connecting them via config- uration files and source code. These architectures evolve as developers add, remove, and modify components, connections, and configurations. These changes can lead to architectural misconfigurations detectable by specification languages and analysis tools. However, these focus on snapshots in time without considering how architectures evolve. By un- derstanding this evolution, we can help specification writers estimate the maintenance burden of their descriptions and promote tools toward the most change-prone elements. To that end, we conduct an empirical study of architectural evolution in open-source ROS systems. We develop a differential, cross-language architectural recovery tool that extracts components, connections, and configurations from source code and apply it to 294 repositories, with 1728 packages across 9914 releases, spanning ROS 1 and ROS 2 in C/C++, Python, and XML. As ROS repositories aggregate multiple packages at different maturity stages, we analyze evolution at the repository and package levels. We find that evolution di- verges across levels, with repositories remaining addition-dominant while mature packages consolidate by removing connections and configurations. Furthermore, changes co-occur across elements: component removals are associated with the removal of their connections and configurations.

Abstract: As software systems become increasingly autonomous and capable of synthesizing their own adaptive strategies, embedding ethical reasoning into self-adaptive controllers raises fundamental ques- tions about trust, responsibility, and moral agency. When adapta- tion unfolds under uncertainty, system decisions inevitably reflect implicit values originating from design choices, training data, and operational contexts. This challenges traditional assumptions about where ethical judgment resides and whether ethics-aware systems should follow fixed moral principles or dynamically negotiate ethi- cal trade-offs at runtime. This paper presents a collective contribution from a commu- nity debate panel of four eminent researchers at the 21st Interna- tional Conference on Software Engineering for Adaptive and Self- Managing Systems (SEAMS), each offering a distinct perspective on the trustworthiness, challenges, implications, and opportunities shaping the emerging horizon of ethics-aware controllers.