A Method of Parameter Estimation in Flexible Jump Diffusion Process Models for Open Source Maintenance Effort Management We focus on several irregular behavior under jump in the operation performance of open source software (OSS). This paper proposes a flexible jump diffusion process model (JDPM) to several jump processes for maintenance effort in the OSS usage associated with version upgrade. Especially, we discuss a method of parameter estimation based on the flexible JDPM considering the unexpected irregular behavior in major version upgrade transition for the project of OSS. Also, it will be useful for the managers of OSS to measure the status of maintenance effort with OSS major version upgrade in terms of the quality management. Moreover, The actual data is analyzed to present the numerical illustrations based on the proposed parameter estimates considering the characteristics with major version upgrade under OSS operation.

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.