Duplicated code, also known as code clones, are one of the malicious 'code smells' that often need to be removed through refactoring for enhancing maintainability. Among all the potential refactoring opportuni...
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ISBN:
(纸本)9780769543475
Duplicated code, also known as code clones, are one of the malicious 'code smells' that often need to be removed through refactoring for enhancing maintainability. Among all the potential refactoring opportunities, the choice and order of a set of refactoring activities may have distinguishable effect on the design/code quality. Moreover, there may be dependencies and conflicts among those refactorings. The organization may also impose priorities on certain refactoring activities. Addressing all these conflicts, priorities, and dependencies, manual formulation of an optimal refactoring schedule is very expensive, if not impossible. Therefore, an automated refactoring scheduler is necessary, which will maximize benefit and minimize refactoring effort. In this paper, we present a refactoring effort model, and propose a constraint programming approach for conflict-aware optimal scheduling of code clone refactoring.
The parameter estimation problem is a widespread and challenging problem in engineering sciences consisting in computing the parameters of a parametric model that fit observed data. Calibration or geolocation can be v...
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ISBN:
(纸本)9781509001644
The parameter estimation problem is a widespread and challenging problem in engineering sciences consisting in computing the parameters of a parametric model that fit observed data. Calibration or geolocation can be viewed as specific parameter estimation problems. In this paper we address the problem of finding all the instances of a parametric model that can explain at least q observations within a given tolerance. The computer vision community has proposed the RANSAC algorithm to deal with outliers in the observed data. This randomized algorithm is efficient but non-deterministic and therefore incomplete. Jaulin et al. proposes a complete and combinatorial algorithm that exhaustively traverses the whole space of parameter vectors to extract the valid model instances. This algorithm is based on interval constraintprogramming methods and on a so called q-intersection operator, a relaxed intersection operator that assumes that at least q observed data are inliers. This paper proposes several improvements to Jaulin et al.'s algorithm. Most of them are generic and some others are dedicated to the shape detection problem used to validate our approach. Compared to Jaulin et al.'s algorithm, our algorithm can guarantee a number of fitted observations in the produced model instances. Also, first experiments in plane and circle recognition highlight speedups of two orders of magnitude.
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