Software systems performance improvement by intelligent data structures customization

It is well known that data structures are essential in obtaining efficient algorithms, having a major importance in the software development process. A proper instantiation of a data structure can greatly impact the performance and the efficiency of the software system. In this paper we are focusing on the problem of customizing data structures instances during the execution of software systems using a supervised learning approach. In order to customize a data structure instance according to the software system’s current execution context, a neural network will be used. We experimentally evaluated our technique on a case study, emphasizing the advantages of the proposed approach. The obtained experimental results highlight the potential of our proposal in using a supervised learning based approach for dynamically configuring data structures instances.