Adaptation of Black-Box Software Components

The globalization of the software market leads to crucial problems for software companies. More competition between software companies arises and leads to the force on companies to develop ever newer software products in ever shortened time interval. Therefor the time to market for software systems is shortened and obviously the product life cycle is shortened too[...]The approach introduced here presents the novel technique together with a supportive environment that enables developers to cope with the adaptability of black-box software components. A supported environment will be designed that checks the compatibility of black-box software components with the assistance of their specifications.Comment: 16 pages, exposed on 2nd "European Conference on Computer Science & Applications" - XA2008, Timisoara, Romani

BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped. However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility. Besides, it is hard to meet a large memory requirement for modern PTMs. In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC). The Coordinator designs input-dependent image-shaped visual prompts, which improves few-shot adaptation and robustness on distribution/location shift. SPSA-GC efficiently estimates the gradient of a target model to update Coordinator. Extensive experiments on 16 datasets demonstrate that BlackVIP enables robust adaptation to diverse domains without accessing PTMs' parameters, with minimal memory requirements. Code: \url{https://github.com/changdaeoh/BlackVIP}Comment: Accepted to CVPR 202

Adaptable transition systems

We present an essential model of adaptable transition systems inspired by white-box approaches to adaptation and based on foundational models of component based systems. The key feature of adaptable transition systems are control propositions, imposing a clear separation between ordinary, functional behaviours and adaptive ones. We instantiate our approach on interface automata yielding adaptable interface automata, but it may be instantiated on other foundational models of component-based systems as well. We discuss how control propositions can be exploited in the specification and analysis of adaptive systems, focusing on various notions proposed in the literature, like adaptability, control loops, and control synthesis

Resolving Architectural Mismatches of COTS Through Architectural Reconciliation

The integration of COTS components into a system under development entails architectural mismatches. These have been tackled, so far, at the component level, through component adaptation techniques, but they also must be tackled at an architectural level of abstraction. In this paper we propose an approach for resolving architectural mismatches, with the aid of architectural reconciliation. The approach consists of designing and subsequently reconciling two architectural models, one that is forward-engineered from the requirements and another that is reverse-engineered from the COTS-based implementation. The final reconciled model is optimally adapted both to the requirements and to the actual COTS-based implementation. The contribution of this paper lies in the application of architectural reconciliation in the context of COTS-based software development. Architectural modeling is based upon the UML 2.0 standard, while the reconciliation is performed by transforming the two models, with the help of architectural design decisions.

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify and analyse a prominent example of adaptive system: robot swarms equipped with obstacle-avoidance self-assembly strategies. The analysis exploits the statistical model checker PVesta