Abstract. The identification of cohesive segments in execution traces is a important step in concept location which, in turns, is of paramount importance for many program-comprehension activities. In this paper, we reformulate the trace segmentation problem as a dynamic programming problem. Differently to approaches based on genetic algorithms, dynamic programming can compute an exact solution with better performance than previous approaches, even on long traces. We describe the new problem formulation and the algorithmic details of our approach. We then compare the performances of dynamic programming with those of a genetic algorithm, showing that dynamic programming reduces dramatically the time required to segment traces, without sacrificing precision and recall; even slightly improving them.

Identifying concepts in execution traces is a task often necessary to support program comprehension or maintenance activities. Several approaches-static, dynamic or hybrid-have been proposed to identify cohesive, meaningful sequence of methods in execution traces. However, none of the proposed approaches is able to label such segments and to identify relations between segments of the same trace.This paper present SCAN (Segment Concept AssigNer) an approach to assign labels to sequences of methods in execution traces, and to identify relations between such segments. SCAN uses information retrieval methods and formal concept analysis to produce sets of words helping the developer to understand the concept implemented by a segment. Specifically, formal concept analysis allows SCAN to discover commonalities between segments in different trace areas, as well as terms more specific to a given segment and high level relations between segments.The paper describes SCAN along with a preliminary manual validation-upon execution traces collected from usage scenarios of JHotDraw and ArgoUML-of SCAN accuracy in assigning labels representative of concepts implemented by trace segments.

Abstract-Identifying concepts in execution traces is a task often necessary to support program comprehension or maintenance activities. Several approaches-static, dynamic or hybrid-have been proposed to identify cohesive, meaningful sequence of methods in execution traces. However, none of the proposed approaches is able to label such segments and to identify relations between segments of the same trace.This paper present SCAN (Segment Concept AssigNer) an approach to assign labels to sequences of methods in execution traces, and to identify relations between such segments. SCAN uses information retrieval methods and formal concept analysis to produce sets of words helping the developer to understand the concept implemented by a segment. Specifically, formal concept analysis allows SCAN to discover commonalities between segments in different trace areas, as well as terms more specific to a given segment and high level relations between segments.The paper describes SCAN along with a preliminary manual validation-upon execution traces collected from usage scenarios of JHotDraw and ArgoUML-of SCAN accuracy in assigning labels representative of concepts implemented by trace segments.

No abstract

Resin-based fiber composite materials have received attention in aerospace composite engineering, particularly in aircraft morphing structures, due to their high mechanical characteristics, such as stiffness, and because of their potential to highly reduce the structural mass of modern aircraft. Aircraft morphing is referred to as the ability of an aircraft’s surface to change its geometry in flight. The modelling of a dynamic morphing wing system is here studied. The morphing wing was controlled using four electric actuators situated inside of the wing model. The main role of these actuators was to modify the wing upper surface shape designed and manufactured with a flexible material, so that the laminar-to-turbulent flow transition point can move closer to the wing trailing edge, thus causing a minimum viscous drag, for various flow conditions. To determine the skin deflections in the four actuators points, both LVDT and dial indicator gages were positioned on the wing. Four Linear Variable Differential Transducers (LVDTs) were used to indicate the positions of the four actuators, and four Dial Indicators gages were positioned on the wing to measure the real deflections of the flexible composite skin in the four actuation points. The relationship between the Dial Indicators’ values and the LVDTs’ values for a same set-point command signal had a nondeterministic and unpredictable behavior (not a linear one). The values of the displacements given by the LVDTs were different than the values given by the Dial Indicators. In this paper, an Artificial Neural Network (ANN) model was investigated created with the aim to predict the displacements of the wing upper surface skin in real time using four actuators. The proposed model was trained using the Extended Great Deluge (EGD) algorithm.