Browsing by Author "Johnson, Franklin"

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    A Spin / Promela Application for Model checking UML Sequence Diagrams
    (2018) Vidal-Silva, Cristian; Villarroel, Rodolfo; Rubio, José Miguel; Johnson, Franklin; Madariaga, Erika; Campos, Camilo; Carter, Luis
    UML sequence diagrams usually represent the behavior of systems execution. Automated verification of UML sequence diagrams’ correctness is necessary because they can model critical algorithmic behaviors of information systems. UML sequence diagrams applications are often on the requirement and design phases of the software development process, and their correctness guarantees the accurate and transparent implementation of software products. The primary goal of this article is to review and improve the translation of basic and complex UML sequence diagrams into Spin / Promela code taking into account behavioral properties and elements of combined fragments of UML sequence diagrams for synchronous and asynchronous messages. This article also redefines a previous proposal for a transition system for UML sequence diagrams by specifying Linear Temporal Logic (LTL) formulas to verify the model correctness. We present an application example of our modeling proposal on a modified version of a traditional case study by using UML sequence diagrams to translate it into Promela code to verify their properties and correctness.
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    Applying FireFly Algorithm to Solve the Problem of Balancing Curricula
    (2019) Rubio, José Miguel; Vidal-Silva, Cristian; Soto, Ricardo; Madariaga, Erika; Johnson, Franklin; Carter, Luis
    The problem of assigning a balanced academic curriculum to academic periods of a curriculum, that is, the balancing curricula, represents a traditional challenge for every educational institution which look for a match among students and professors. This article proposes a solution for the balancing curricula problem using an optimization technique based on the attraction of fireflies (FA) meta-heuristic. We perform a set of test and real instances to measure the performance of our solutionproposal just looking to deliver a system that will simplify the process of designing a curricular network in higher education institutions. The obtained results show that our solution achieves a fairly fast convergence and finds the optimum known in most of the tests carried out.
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    Aspect-Combining Functions for Modular MapReduce Solutions
    (2018) Vidal-Silva, Cristian; Villarroel, Rodolfo; Rubio, José Miguel; Johnson, Franklin; Madariaga, Érika; Urzúa, Alberto; Carter, Luis; Campos-Valdés, Camilo; López-Cortés, Xaviera A.
    MapReduce represents a programming framework for modular Big Data computation that uses a function map to identify and target intermediate data in the mapping phase, and a function reduce to summarize the output of the map function and give a final result. Because inputs for the reduce function depend on the map function’s output to decrease the communication traffic of the output of map functions to the input of reduce functions, MapReduce permits defining combining function for local aggregation in the mapping phase. MapReduce Hadoop solutions do not warrant the combining functioning application. Even though there exist proposals for warranting the combining function execution, they break the modular nature of MapReduce solutions. Because Aspect-Oriented Programming (AOP) is a programming paradigm that looks for the modular software production, this article proposes and apply Aspect-Combining function, an AOP combining function, to look for a modular MapReduce solution. The Aspect-Combining application results on MapReduce Hadoop experiments highlight computing performance and modularity improvements and a warranted execution of the combining function using an AOP framework like AspectJ as a mandatory requisite.