All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.

Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Furthermore, the systematic analysis of the research literature provided in this review aims to help the research community in consolidating the existing research efforts and deriving a research agenda for future developments.

Model checking provides a powerful means to assert and verify desired system properties. But, for the verification process to become feasible, a correct formulation of these properties in a temporal logic is necessary - a potential barrier to application in practice. Research on property specification has supplied us with rich pattern catalogs that capture commonly occurring system properties in different temporal logics. Furthermore, these property specification pattern catalogs usually offer both a structured English grammar to facilitate the pattern selection and an associated template solutions to express the properties formally. Yet, the actual use of property specification patterns remains cumbersome, due to limited tool support. For this reason, we have developed the Property Specification Pattern Wizard (PSPWizard), a framework that defines an interface for the currently accepted property specification pattern libraries. PSPWizard consists of two main building blocks: a mapping generator that weaves a given pattern library with a target logic and a GUI front-end to the structured English grammar tailored to those patterns that are supported in the target logic.

Probabilistic models are crucial in the quantification of non-functional attributes in safety-and mission-critical software systems. These models are often re-evaluated in assessing the design decisions. Evaluation of such models is computationally expensive and exhibits exponential complexity with the problem size. This research aims at constructing an incremental quality evaluation framework and delta evaluation scheme to address this issue. The proposed technique will provide a computational advantage for the probabilistic quality evaluations enabling their use in automated design space exploration by architecture optimization algorithms. The expected research outcomes are to be validated with a range of realistic architectures and case studies from automotive industry.

Probabilistic models are widely used in Architecture-based reliability prediction in software intensive systems. However, for most of the cases, it is computationally expensive to compute the reliability metrics and re-compute them once the system has evolved or is used in a different environment. In this paper, we introduce an efficient computation method for Discrete Time Markov Chain based abstractions, which computes reliability metrics once, and we provide an incremental technique to recompute these metrics in case of a single change in the reliability evaluation model. As a result, fast an efficient reliability computation can be provided for scenarios like design-time architecture optimization and run time adaptation. An experimental validation of the new method shows a significant improvement in terms of computation time required to re-evaluate an evolved architecture.

Decisions regarding the mapping of software components to hardware nodes affect the quality of the resulting system. Making these decisions is hard when considering the ever-growing complexity of the search space, as well as conflicting objectives and constraints. An automation of the solution space exploration would help not only to make better decisions but also to reduce the time of this process. In this paper, we propose to employ Ant Colony Optmisation (ACO) as a multi-objective optimisation strategy. The constructive approach is compared to an iterative optimisation procedure - a Genetic Algorithm (GA) adaptation - and was observed to perform suprisingly similar, although not quite on a par with the GA, when validated based on a series of experiments.