It is our pleasure to announce another CS group meeting for the next Friday, September 4, at 11:30, Room B.
Speaker. Catia Trubiani
Title. Exploiting Traceability Uncertainty between Software Architectural Models and Performance Analysis Results
Abstract. While software architecture performance analysis is a well studied field, it is less understood how the analysis results (i.e., mean values, variances, and/or probability distributions) trace back to the architectural model elements (i.e., software components, interactions among components, deployment nodes). Yet, understanding this traceability is critical for understanding the analysis result in context of the architecture.
The goal of this paper is to automate the traceability between software architectural models and performance analysis results by investigating the uncertainty while bridging these two domains. Our approach makes use of performance antipatterns to deduce the logical consequences between the architectural elements and analysis results and automatically build a graph of traces to identify the most critical causes of performance flaws.
We developed a tool that jointly considers SOftware and PErformance concepts (SoPeTraceAnalyzer), and it automatically builds model-to-results traceability links. The benefit of the tool is illustrated by means of a case study in the e-health domain.
Speaker. Lorenzo Severini
Title. On the maximum betweenness augmentation problem
Abstract. The betweenness is a well-known measure of centrality of a node in a network. We consider the problem of determining how much a node can increase its betweenness centrality by creating a limited amount of new edges incident to it. If the graph is directed, this problem does not admit a polynomial-time approximation scheme (unless P = NP) and a simple greedy approximation algorithm guarantees an almost tight approximation ratio. In this paper we focus on the undirected graph case: we show that also in this case the problem does not admit a polynomial-time approximation scheme (unless P = NP). Moreover, we show that, differently from the directed case, the greedy algorithm can have an unbounded approximation ratio. In order to test the practical performance of the greedy algorithm, we experimentally measured its efficiency in term of ranking improvement, comparing it with another algorithm that simply adds edges to the nodes that have highest betweenness.
Our experiments show that the greedy algorithm adds only few edges in order to increase the betweenness of a node and to reach the top positions in the ranking. Moreover, the greedy algorithm outperforms the second approach.