References of "Mariani, Leonardo"
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See detailTkT: Automatic Inference of Timed and Extended Pushdown Automata
Pastore, Fabrizio UL; Micucci, Daniela; Guzman, Michell et al

in IEEE Transactions on Software Engineering (2020)

To mitigate the cost of manually producing and maintaining models capturing software specifications, specification mining techniques can be exploited to automatically derive up-to-date models that ... [more ▼]

To mitigate the cost of manually producing and maintaining models capturing software specifications, specification mining techniques can be exploited to automatically derive up-to-date models that faithfully represent the behavior of software systems. So far, specification mining solutions focused on extracting information about the functional behavior of the system, especially in the form of models that represent the ordering of the operations. Well-known examples are finite state models capturing the usage protocol of software interfaces and temporal rules specifying relations among system events. Although the functional behavior of a software system is a primary aspect of concern, there are several other non-functional characteristics that must be typically addressed jointly with the functional behavior of a software system. Efficiency is one of the most relevant characteristics. In fact, an application delivering the right functionalities inefficiently has a big chance to not satisfy the expectation of its users. Interestingly, the timing behavior is strongly dependent on the functional behavior of a software system. For instance, the timing of an operation depends on the functional complexity and size of the computation that is performed. Consequently, models that combine the functional and timing behaviors, as well as their dependencies, are extremely important to precisely reason on the behavior of software systems. In this paper, we address the challenge of generating models that capture both the functional and timing behavior of a software system from execution traces. The result is the Timed k-Tail (TkT) specification mining technique, which can mine finite state models that capture such an interplay: the functional behavior is represented by the possible order of the events accepted by the transitions, while the timing behavior is represented through clocks and clock constraints of different nature associated with transitions. Our empirical evaluation with several libraries and applications show that TkT can generate accurate models, capable of supporting the identification of timing anomalies due to overloaded environment and performance faults. Furthermore, our study shows that TkT outperforms state-of-the-art techniques in terms of scalability and accuracy of the mined models. [less ▲]

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See detailCPSDebug: a tool for explanation of failures in cyber-physical systems
Bartocci, Ezio; Manjunath, Niveditha; Mariani, Leonardo et al

in CPSDebug: a tool for explanation of failures in cyber-physical systems (2020)

Debugging Cyber-Physical System models is often challenging, as it requires identifying a potentially long, complex and heterogenous combination of events that resulted in a violation of the expected ... [more ▼]

Debugging Cyber-Physical System models is often challenging, as it requires identifying a potentially long, complex and heterogenous combination of events that resulted in a violation of the expected behavior of the system. In this paper we present CPSDebug, a tool for supporting designers in the debugging of failures in MAT- LAB Simulink/Stateflow models. CPSDebug implements a gray-box approach that combines testing, specification mining, and failure analysis to identify the causes of failures and explain their propagation in time and space. The evaluation of the tool, based on multiple usage scenarios and faults and direct feedback from engineers, shows that CPSDebug can effectively aid engineers during debugging tasks. [less ▲]

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See detailIn-The-Field Monitoring of Functional Calls: Is It Feasible?
Cornejo Olivares, Oscar Eduardo UL; Briola, Daniela; Micucci, Daniela et al

in Journal of Systems and Software (2020)

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See detailZoomIn: Discovering Failures by Detecting Wrong Assertions
Pastore, Fabrizio UL; Mariani, Leonardo

in Proceedings of the 37th International Conference on Software Engineering (ICSE) (2015, May)

Automatic testing, although useful, is still quite ineffective against faults that do not cause crashes or uncaught exceptions. In the majority of the cases automatic tests do not include oracles, and ... [more ▼]

Automatic testing, although useful, is still quite ineffective against faults that do not cause crashes or uncaught exceptions. In the majority of the cases automatic tests do not include oracles, and only in some cases they incorporate assertions that encode the observed behavior instead of the intended behavior, that is if the application under test produces a wrong result, the synthesized assertions will encode wrong expectations that match the actual behavior of the application. In this paper we present ZoomIn, a technique that extends the fault-revealing capability of test case generation techniques from crash-only faults to faults that require non-trivial oracles to be detected. ZoomIn exploits the knowledge encoded in the manual tests written by developers and the similarity between executions to automatically determine an extremely small set of suspicious assertions that are likely wrong and thus worth manual inspection. Early empirical results show that ZoomIn has been able to detect 50% of the analyzed non-crashing faults in the Apache Commons Math library requiring the inspection of less than 1.5% of the assertions automatically generated by EvoSuite. [less ▲]

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See detailDo Automatically Generated Test Cases Make Debugging Easier? An Experimental Assessment of Debugging Effectiveness and Efficiency
Ceccato, Mariano; Marchetto, Alessandro; Mariani, Leonardo et al

in The ACM Transactions on Software Engineering and Methodology (TOSEM) (2015), 25(1), 51--538

Detailed reference viewed: 114 (17 UL)