Prediction of interactions in self-adaptive software

Abstract

Background. The article is devoted to the problem of predicting interactions between individual components in self-adaptive software systems. Two types of interactions are considered as possible unaccounted for interactions: the “inclusion” relation, in which the presence of a certain component in the configuration of the software product also requires the automatic inclusion of another in it; the “exclusion” relation, in which the presence of one component in the configuration excludes the presence of another. Since selfadaptation implies minimal participation of a professional developer in restructuring and building up new system functions, additional tools are needed to allow the system to also independently verify the changed structure. Since the mathematical apparatus of selfadaptation developed earlier by the authors of the article is based on the application of graph theory, the new proposed solution is an improved version of this apparatus using artificial intelligence methods. Materials and methods. As the main methods, artificial intelligence and neural network technologies are used, in particular, methods for predicting relationships in graph structures, machine learning methods. Results. The main results include a method for predicting possible interactions in a self-adaptive software system based on predicting relationships in graph structures. Conclusions. The proposed method is an improvement of the theoretical apparatus of self-adaptation of software systems developed earlier by the authors, based on graph formalization of the behavioral structure of the program. Since self-adaptation in this approach implies an automatic change in the structure, for its regulation, the use of artificial intelligence algorithms is envisaged, which makes it possible to evaluate new potential connections in terms of their usefulness or destructiveness for the system as a whole.

Keywords

• predicting of software interactions
• self-adaptive software
• software engineering
• neural networks
• artificial intelligence
• link prediction