User Interaction with a Technology Evolution Model to Select Most Viable Enterprise Information Systems One of alternative ways to achieve a competitive advantage by an enterprise in the ICT area is to select the most prospective information system (IS) as a seed technology. The latter is defined as a first technology from a given area implemented in an enterprise, that determines or strongly influences the choice of further technologies from the same area. Seed ICT selection is particularly relevant for enterprises whose core business activity relies on online service provision. In this paper we present a userrequirementbased interaction scheme of an application that models the evolution of enterprise ISs, taking into account the production patterns of software releases, the functionalities offered, the usersu0027 community growth trends, and other factors. The evolution is modeled by hybrid discretetimediscreteevent systems, and multimodels built with them. The technology choice is supported by multicriteria ranking algorithms that apply IS innovativeness forecasts. Moreover, this evolution model of enterprise IS can handle different uncertainties and cyber security issues. We present the use cases and scenarios, model management, and the knowledge base that stores time series, IS characteristics and forecasts. The use of this application is illustrated by a reallife example of three popular opensource CMSs. We conclude that an informed choice of the first IS and the corresponding enterprise architecture as a seed technology is particularly relevant for startups and SMEs. An extended variant of the above application may also provide decision support to developer teams seeking software evolution models to discover and apply the bestpossible technology development and market strategies.

Accurate Modeling of Performance Histories for Evolving Software Systems Learning from the history of a software systemu0027s performance behavior does not only help discovering and locating performance bugs, but also identifying evolutionary performance patterns and general trends, such as when technical debt accumulates. Exhaustive regression testing is usually impractical, because rigorous performance benchmarking requires executing a realistic workload per revision, which results in large execution times. In this paper, we propose a novel active revision sampling approach, which aims at tracking and understanding a systemu0027s performance history by approximating the performance behavior of a software system across all of its revisions. In a nutshell, we iteratively sample and measure the performance of specific revisions that help us building an exact performanceevolution model, and we use Gaussian Process models to assess in which revision ranges our model is most uncertain with the goal to sample further revisions for measurement. We have conducted an empirical analysis of the evolutionary performance behavior modeled as a time series of the histories of six realworld software systems. Our evaluation demonstrates that Gaussian Process models are able to accurately estimate the performanceevolution history of realworld software systems with only few measurements and to reveal interesting behaviors and trends.