http://sedici.unlp.edu.ar:80/handle/10915/64864 2026-06-15T02:13:40Z 2026-06-15T02:13:40Z Mongiovi, Melina http://sedici.unlp.edu.ar:80/handle/10915/64921 2020-01-01T20:03:46Z 2017-01-01T00:00:00Z

Objeto de conferencia III Concurso Latinoamericano de Tesis de Doctorado (CLTD-CLEI)- JAIIO 46 (Córdoba, 2017). Defining and implementing refactorings is a nontrivial task since it is difficult to define preconditions to guarantee that the transformation preserves the program behavior. Therefore, refactoring engines may apply incorrect transformations in which the resulting program does not compile, preserve behavior, or follow the refactoring definitions. These engines may also prevent correct transformations due to overly strong preconditions. We find that 84% of the test suites of Eclipse and JRRT are concerned to detect those kinds of bugs. However, the engines still have them. Researchers have proposed a number of techniques for testing refactoring engines. Nevertheless, they may have limitations related to the bug type, program generation, time consumption, and number of refactoring engines necessary to evaluate the implementations. We propose and implement a technique to scale testing of refactoring engines. We improve expressiveness of a program generator and use a technique to skip some test inputs to improve performance. Moreover, we propose new oracles to detect behavioral changes using change impact analysis, overly strong preconditions by disabling preconditions, and transformation issues. We evaluate our technique in 28 refactoring implementations of Java (Eclipse and JRRT) and C (Eclipse) and find 119 bugs. The technique reduces the time in 96% using skips while missing only 6% of the bugs. Additionally, it finds the first failure in general in a few seconds using skips. Finally, we evaluate our proposed technique by using other test inputs, such as the input programs of Eclipse and JRRT refactoring test suites. We find 31 bugs not detected by the developers.

2017-01-01T00:00:00Z Defining and implementing refactorings is a nontrivial task since it is difficult to define preconditions to guarantee that the transformation preserves the program behavior. Therefore, refactoring engines may apply incorrect transformations in which the resulting program does not compile, preserve behavior, or follow the refactoring definitions. These engines may also prevent correct transformations due to overly strong preconditions. We find that 84% of the test suites of Eclipse and JRRT are concerned to detect those kinds of bugs. However, the engines still have them. Researchers have proposed a number of techniques for testing refactoring engines. Nevertheless, they may have limitations related to the bug type, program generation, time consumption, and number of refactoring engines necessary to evaluate the implementations. We propose and implement a technique to scale testing of refactoring engines. We improve expressiveness of a program generator and use a technique to skip some test inputs to improve performance. Moreover, we propose new oracles to detect behavioral changes using change impact analysis, overly strong preconditions by disabling preconditions, and transformation issues. We evaluate our technique in 28 refactoring implementations of Java (Eclipse and JRRT) and C (Eclipse) and find 119 bugs. The technique reduces the time in 96% using skips while missing only 6% of the bugs. Additionally, it finds the first failure in general in a few seconds using skips. Finally, we evaluate our proposed technique by using other test inputs, such as the input programs of Eclipse and JRRT refactoring test suites. We find 31 bugs not detected by the developers. Oliveira, Lucas de Souza, Cid Carvalho de Yunes, Tallys http://sedici.unlp.edu.ar:80/handle/10915/64920 2020-01-01T20:03:45Z 2017-01-01T00:00:00Z

Objeto de conferencia III Concurso Latinoamericano de Tesis de Doctorado (CLTD-CLEI)- JAIIO 46 (Córdoba, 2017). Estudamos neste doutorado o problema dos árbitros viajantes (TUP, do inglês traveling umpire problem), que consiste em um problema de otimizacão baseado no problema real de alocacão de árbitros às partidas da Liga Profissional de Beisebol dos Estados Unidos. O TUP recebe como entrada um torneio round robin duplo e tem como objetivo atribuir árbitros ás partidas deste torneio minimizando a distância total viajada por eles durante toda a competicão e respeitando restricões que impõem que cada árbitro não apite jogos de um mesmo time frequentemente e apite ao menos um jogo na sede de cada time. Demonstramos que o TUP é um problema NP-completo, fechando esta questão em relacão à sua complexidade que ficou em aberto durante sete anos. Também introduzimos duas novas formulacões matemáticas e uma heurística relax-and-fix para este problema. As análises de resultados computacionais comprovam que as formulacões matemáticas e a heurística relax-and-fix produzem limitantes inferiores e superiores de excelente qualidade para o TUP, melhorando diversos resultados da literatura.

2017-01-01T00:00:00Z Estudamos neste doutorado o problema dos árbitros viajantes (TUP, do inglês traveling umpire problem), que consiste em um problema de otimizacão baseado no problema real de alocacão de árbitros às partidas da Liga Profissional de Beisebol dos Estados Unidos. O TUP recebe como entrada um torneio round robin duplo e tem como objetivo atribuir árbitros ás partidas deste torneio minimizando a distância total viajada por eles durante toda a competicão e respeitando restricões que impõem que cada árbitro não apite jogos de um mesmo time frequentemente e apite ao menos um jogo na sede de cada time. Demonstramos que o TUP é um problema NP-completo, fechando esta questão em relacão à sua complexidade que ficou em aberto durante sete anos. Também introduzimos duas novas formulacões matemáticas e uma heurística relax-and-fix para este problema. As análises de resultados computacionais comprovam que as formulacões matemáticas e a heurística relax-and-fix produzem limitantes inferiores e superiores de excelente qualidade para o TUP, melhorando diversos resultados da literatura. Barba Maggi, Lida http://sedici.unlp.edu.ar:80/handle/10915/64919 2020-01-01T20:03:45Z 2017-01-01T00:00:00Z

Objeto de conferencia III Concurso Latinoamericano de Tesis de Doctorado (CLTD-CLEI)- JAIIO 46 (Córdoba, 2017). Time series are valuable sources of information for supporting planning activities. Transport, fishery, economy and finances are predominant sectors concerned into obtaining information in advance to improve their productivity and efficiency. During the last decades diverse linear and nonlinear forecasting models have been developed for attending this demand. However the achievement of accuracy follows being a challenge due to the high variability of the most observed phenomena. In this research are proposed two decomposition methods based on Singular Value Decomposition of a Hankel matrix (HSVD) in order to extract components of low and high frequency from a nonstationary time series. The proposed decomposition is used to improve the accuracy of linear and nonlinear autoregressive models. The evaluation of the proposed forecasters is performed through data coming from transport sector and fishery sector. Series of injured persons in traffic accidents of Santiago and Valparaíso and stock of sardine and anchovy of central-south Chilean coast are used. Further, for comparison purposes, it is evaluated the forecast accuracy reached by two decomposition techniques conventionally used, Singular Spectrum Analysis (SSA) and decomposition based on Stationary Wavelet Transform (SWT), both joint with linear and nonlinear autoregressive models. The experiments shown that the proposed methods based on Singular Value Decomposition of a Hankel matrix in conjunction with linear or nonlinear models reach the best accuracy for one-step and multi-step ahead forecasting of the studied time series.

2017-01-01T00:00:00Z Time series are valuable sources of information for supporting planning activities. Transport, fishery, economy and finances are predominant sectors concerned into obtaining information in advance to improve their productivity and efficiency. During the last decades diverse linear and nonlinear forecasting models have been developed for attending this demand. However the achievement of accuracy follows being a challenge due to the high variability of the most observed phenomena. In this research are proposed two decomposition methods based on Singular Value Decomposition of a Hankel matrix (HSVD) in order to extract components of low and high frequency from a nonstationary time series. The proposed decomposition is used to improve the accuracy of linear and nonlinear autoregressive models. The evaluation of the proposed forecasters is performed through data coming from transport sector and fishery sector. Series of injured persons in traffic accidents of Santiago and Valparaíso and stock of sardine and anchovy of central-south Chilean coast are used. Further, for comparison purposes, it is evaluated the forecast accuracy reached by two decomposition techniques conventionally used, Singular Spectrum Analysis (SSA) and decomposition based on Stationary Wavelet Transform (SWT), both joint with linear and nonlinear autoregressive models. The experiments shown that the proposed methods based on Singular Value Decomposition of a Hankel matrix in conjunction with linear or nonlinear models reach the best accuracy for one-step and multi-step ahead forecasting of the studied time series.