Abstract In this paper, we propose a bottom‐up approach for the verification of systems with modular structure: we prove that when the modules are composed in specific ways, the complete software system verifies a composition of the properties each component does. We focus on the process of upgrading systems with new functionalities, where the validity of old requirements needs to be ensured, but also an understanding of the new properties the upgraded system would enjoy is useful. In this work, we assume each component to be specified by a CCS process, and the properties to be expressed by selective mu‐calculus formulae. Copyright © 2007 John Wiley & Sons, Ltd.

Large multi-platform, multi-million lines of codes software systems evolve to cope with new platform or to meet user ever changing needs. While there has been several studies focused on the similarity of code fragments or modules, few studies addressed the need to monitor the overall system evolution. Meanwhile, the decision to evolve or to re-factor a large software system needs to be supported by high level information, representing the system overall picture, abstracting from unnecessary details. This paper proposes to extend the concept of similarity of code fragments to quantify similarities at the release/system level. Similarities are captured by four software metrics representative of the commonalities and differences within and among software artifacts. To show the feasibility of characterizing large software system with the new metrics, 365 releases of the Linux kernel were analyzed. The metrics, the experimental results as well as the lessons learned are presented in the paper.

When a software system critical for an organization exhibits a problem during its operation, it is relevant to fix it in a short period of time, to avoid serious economical losses. The problem is therefore noticed by the organization in charge of the maintenance, and it should be correctly and quickly dispatched to the right maintenance team. We propose to automatically classify incoming maintenance requests (also said tickets), routing them to specialized maintenance teams. The final goal is to develop a router working around the clock, that, without human intervention, dispatches incoming tickets with the lowest misclassification error, measured with respect to a given routing policy. 6000 maintenance tickets from a large, multi-site, software system, spanning about two years of system in-field operation, were used to compare and assess the accuracy of different classification approaches (i.e., Vector Space model, Bayesian model, support vectors, classification trees and k-nearest neighbor classification). The application and the tickets were divided into eight areas and pre-classified by human experts. Preliminary results were encouraging, up to 84% of the incoming tickets were correctly classified.