Stefano Di Alesio

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Safety-critical Real Time Embedded Systems (RT-ESs) are usually subject to strict timing and performance requirements that must be satisfied for the system to be deemed safe. In this paper, we use effective search strategies whose goal is finding worst case scenarios with respect to deadline misses. Such scenarios can in turn be used to test the target RTES(More)
Tasks in real-time embedded systems (RTES) are often subject to hard deadlines that constrain how quickly the system must react to external inputs. These inputs and their timing vary in a large domain depending on the environment state and can never be fully predicted prior to system execution. Therefore, approaches for stress testing must be developed to(More)
Software safety certification needs to address non-functional constraints with safety implications, e.g., deadlines, throughput, and CPU and memory usage. In this paper, we focus on CPU usage constraints and provide a framework to support the derivation of test cases that maximize the chances of violating CPU usage requirements. We develop a conceptual(More)
Past research has proposed association rule mining as a means to uncover the evolutionary coupling from a system's change history. These couplings have various applications, such as improving system decomposition and recommending related changes during development. The <i>strength</i> of the coupling can be characterized using a variety of(More)
Real-Time Embedded Systems (RTES) in safety-critical domains, such as maritime and energy, must satisfy strict performance requirements to be deemed safe. Therefore, such systems have to be thoroughly tested to ensure their correct behavior even under the worst operating conditions. In this paper, we address the need of deriving worst case scenarios with(More)
Safety-critical real-time applications are typically subject to stringent timing constraints which are dictated by the surrounding physical environments. Specifically, tasks in these applications need to finish their execution before given deadlines, otherwise the system is deemed unsafe. It is therefore important to test real-time systems for deadline(More)
Software change impact analysis aims to find artifacts potentially affected by a change. Typical approaches apply language-specific static or dynamic dependence analysis, and are thus restricted to homogeneous systems. This restriction is a major drawback given today's increasingly heterogeneous software. Evolutionary coupling has been proposed as a(More)
Association rule mining is an unsupervised learning technique that infers relationships among items in a data set. This technique has been successfully used to analyze a system's change history and uncover <em>evolutionary coupling</em> between system artifacts. Evolutionary coupling can, in turn, be used to <em>recommend</em> artifacts that are potentially(More)