Adenilso Simao

Roland Groz, German Eduardo Vega Baez, Adenilso Simao et al.

Extended finite state machines (EFSMs) model stateful systems with internal data variables and have numerous applications in software engineering. A major advantage of this type of model lies in its ability to model both the data flow and the data-dependent control behaviour. In the absence of such models, it is desirable to reverse-engineer them by observing the system's behaviour. However, existing approaches generally require the ability to reset the system during inference, or can only handle situations where the control flow depends exclusively on the input parameters, and not on the values of the stored data. In this work, we present a black-box active learning algorithm that infers EFSMs with guards and registers, and which significantly relaxes the assumptions that have to be made about the system in comparison to previous attempts.

Faimison Porto, Leandro Minku, Emilia Mendes et al.

The prediction of defects in a target project based on data from external projects is called Cross-Project Defect Prediction (CPDP). Several methods have been proposed to improve the predictive performance of CPDP models. However, there is a lack of comparison among state-of-the-art methods. Moreover, previous work has shown that the most suitable method for a project can vary according to the project being predicted. This makes the choice of which method to use difficult. We provide an extensive experimental comparison of 31 CPDP methods derived from state-of-the-art approaches, applied to 47 versions of 15 open source software projects. Four methods stood out as presenting the best performances across datasets. However, the most suitable among these methods still varies according to the project being predicted. Therefore, we propose and evaluate a meta-learning solution designed to automatically select and recommend the most suitable CPDP method for a project. Our results show that the meta-learning solution is able to learn from previous experiences and recommend suitable methods dynamically. When compared to the base methods, however, the proposed solution presented minor difference of performance. These results provide valuable knowledge about the possibilities and limitations of a meta-learning solution applied for CPDP.

Adenilso Simao, Alexandre Petrenko

Testing from Input/Output Transition Systems has been intensely investigated. The conformance between the implementation and the specification is often determined by the so-called ioco-relation. However, generating tests for ioco is usually hindered by the problem of conflicts between inputs and outputs. Moreover, the generation is mainly based on nondeterministic methods, which may deliver complete test suites but require an unbounded number of executions. In this paper, we investigate whether it is possible to construct a finite test suite which is complete in a predefined fault domain for the classical ioco relation even in the presence of input/output conflicts. We demonstrate that it is possible under certain assumptions about the specification and implementation, by proposing a method for complete test generation, based on a traditional method developed for FSM.