Abstract Models play a critical role in supporting Verification and Validation activities. However, they are often unavailable in practice, such as for legacy systems or third-party components. Model learning addresses this gap through two main approaches: passive learning, which infers models from existing execution traces, and active learning, which interacts with the system under learning (SUL) to generate more general models, but at higher computational cost. In this work, we propose a novel application of Transformer architectures to implicitly learn generic system models from execution traces alone, combining the efficiency of passive learning with the generality of active learning. We design and evaluate two Transformer-based architectures: one focused on exploitation, achieving over $$\varvec{95\%}$$ valid trace generation; and another focused on exploration, generating approximately $$\varvec{60\%}$$ novel, previously unseen traces. Our methods outperform state-of-the-art model learning techniques, demonstrating that Transformers can achieve results comparable to active learning while requiring significantly fewer resources.

This paper presents an exact search-based technique to minimize test suites while maintaining their mutation coverage. The minimization of test suites is a hard problem whose solution is important both to reduce the cost of mutation testing and to precisely assess the quality of existing test suites. This problem can be addressed with Search-Based Software Engineering (SBSE) techniques, including metaheuristics and exact techniques. We have applied Integer Linear Programming (ILP) as an exact technique to reduce the effort of testing with very promising results. Our technique can be adapted to different formalisms but this paper focuses on testing WS-BPEL compositions, as it poses several interesting problems. Despite the fact that web service compositions are relatively small, as they just orchestrate web services, their execution can be very expensive because the deployment and execution of web services, and the underlying infrastructure, are not trivial. Therefore, although test suites for the compositions themselves are also usually small, it is fundamental to reduce, as much as possible and without losing coverage, their size.

In this paper, we propose a technique to optimize dynamic communication network topologies. Due to recent developments in virtualization technologies like hardware virtualization, software defined networking (SDN), and network function virtualization (NFV), a completely new way of automated network optimization becomes possible. We propose a technique to enable hybrid evolutionary dynamic optimization of networking infrastructures based on changes to its topology or state. Networks have numerous, partially competing, attributes, which might be subject to optimization. Here, we focus exemplarily on the problem of finding the optimal amount and position of monitors in a computer network in order to monitor the whole network traffic. This dynamic monitor selection problem is generalizable to the well-known NP-complete vertex cover problem. Experiments are conducted on different real-world networks using an evolutionary search heuristic. We studied the behavior of the proposed approach using 3 different change levels of the problem instances. Experimental results show that our proposed approach provides network configurations having a sufficiently high quality in reasonable time for all problem instances and change levels. Thus, using the proposed technique may be one helpful step towards an automated self-adapting network optimization.

In this paper, we propose a hybrid evolutionary algorithm (EA) for the optimization of efficient monitoring in dynamic communication networks. The first step towards improving communication infrastructures is gathering information about the current situation. One part of collecting this information is to implement an adequate monitoring in the network, i.e., the optimal positions and amount of monitoring devices, in order to analyze communication flows.

Summary Mutation testing is a successful testing technique based on fault injection. However, it can be very costly, and several cost‐reduction techniques for reducing the number of mutants have been proposed in the literature. Cost reduction can be aided by an analysis of mutation operators, but this requires the definition of specialized metrics. Several metrics have been proposed before, although their effectiveness and relative merits are not easy to assess. A step ahead in the evaluation of mutation‐reduction techniques would be a better metric to determine objectively the quality of a set of mutants with respect to a given test suite. This work introduces such a metric, which is naturally extended to mutation operators and may be used to reduce the number of mutants, particularly of equivalent mutants. Finally, a firm mutation analysis tool for WS‐BPEL service compositions is presented, and experimental results obtained by comparing different metrics on several compositions are presented. Copyright © 2014 John Wiley & Sons, Ltd.

The nature of the requirements analysis problem, based as it is on uncertain and often inaccurate estimates of costs and effort, makes sensitivity analysis important. Sensitivity analysis allows the decision maker to identify those requirements and budgets that are particularly sensitive to misestimation. However, finding scalable sensitivity analysis techniques is not easy because the underlying optimization problem is NP-hard. This article introduces an approach to sensitivity analysis based on exact optimization. We implemented this approach as a tool, O ATSAC , which allowed us to experimentally evaluate the scalability and applicability of Requirements Sensitivity Analysis (RSA). Our results show that O ATSAC scales sufficiently well for practical applications in Requirements Sensitivity Analysis. We also show how the sensitivity analysis can yield insights into difficult and otherwise obscure interactions between budgets, requirements costs, and estimate inaccuracies using a real-world case study.