Liberate Abstract Garbage Collection from the Stack by Decomposing the Heap

Abstract garbage collection and the use of pushdown systems each enhance the precision of control-flow analysis (CFA). However, their respective needs conflict: abstract garbage collection requires the stack but pushdown systems obscure it. Though several existing techniques address this conflict, none take full advantage of the underlying interplay. In this paper, we dissolve this conflict with a technique which exploits the precision of pushdown systems to decompose the heap across the continuation.This technique liberates abstract garbage collection from the stack, increasing its effectiveness and the compositionality of its host analysis. We generalize our approach to apply compositional treatment to abstract timestamps which induces the context abstraction of m-CFA, an abstraction more precise than k-CFA’s for many common programming patterns.

Control-flow analysis of procedural skills competencies in medical training through process mining

BackgroundProcedural skills are key to good clinical results, and training in them involves a significant amount of resources. Control-flow analysis (ie, the order in which a process is performed) can provide new information for those who train and plan procedural training. This study outlines the steps required for control-flow analysis using process mining techniques in training in an ultrasound-guided internal jugular central venous catheter placement using a simulation.MethodsA reference process model was defined through a Delphi study, and execution data (event logs) were collected from video recordings from pretraining (PRE), post-training (POST) and expert (EXP) procedure executions. The analysis was performed to outline differences between the model and executions. We analysed rework (activity repetition), alignment-based fitness (conformance with the ideal model) and trace alignment analysis (visual ordering pattern similarities).ResultsExpert executions do not present repetition of activities (rework). The POST rework is lower than the PRE, concentrated in the steps of the venous puncture and guidewire placement. The adjustment to the ideal model measure as alignment-based fitness, expressed as a median (25th–75th percentile) of PRE 0.74 (0.68–0.78) is less than POST 0.82 (0.76–0.86) and EXP 0.87 (0.82–0.87). There are no significant differences between POST and EXP. The graphic analysis of alignment and executions shows a progressive increase in order from PRE to EXP executions.ConclusionProcess mining analysis is able to pinpoint more difficult steps, assess the concordance between reference mode and executions, and identify control-flow patterns in procedural training courses.