SourcererCC: Scaling Code Clone Detection to Big Code

Despite a decade of active research, there is a marked lack in clone detectors that scale to very large repositories of source code, in particular for detecting near-miss clones where significant editing activities may take place in the cloned code. We present SourcererCC, a token-based clone detector that targets three clone types, and exploits an index to achieve scalability to large inter-project repositories using a standard workstation. SourcererCC uses an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone. We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks, (1) a large benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (250MLOC) using a standard workstation.Comment: Accepted for publication at ICSE'16 (preprint, unrevised

Structured Review of the Evidence for Effects of Code Duplication on Software Quality

This report presents the detailed steps and results of a structured review of code clone literature. The aim of the review is to investigate the evidence for the claim that code duplication has a negative effect on code changeability. This report contains only the details of the review for which there is not enough place to include them in the companion paper published at a conference (Hordijk, Ponisio et al. 2009 - Harmfulness of Code Duplication - A Structured Review of the Evidence)

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

In Vitro Maturation of a Humanized Shark VNAR Domain to Improve Its Biophysical Properties to Facilitate Clinical Development

Acknowledgments: The authors would like to acknowledge the funding support for this work from Scottish Enterprise [VNAR_001(2012)] and the Biotechnology and Biological Sciences Research Council (BB/K010905/1).Peer reviewedPublisher PD

Genetically engineered minipigs model the major clinical features of human neurofibromatosis type 1.

Neurofibromatosis Type 1 (NF1) is a genetic disease caused by mutations in Neurofibromin 1 (NF1). NF1 patients present with a variety of clinical manifestations and are predisposed to cancer development. Many NF1 animal models have been developed, yet none display the spectrum of disease seen in patients and the translational impact of these models has been limited. We describe a minipig model that exhibits clinical hallmarks of NF1, including café au lait macules, neurofibromas, and optic pathway glioma. Spontaneous loss of heterozygosity is observed in this model, a phenomenon also described in NF1 patients. Oral administration of a mitogen-activated protein kinase/extracellular signal-regulated kinase inhibitor suppresses Ras signaling. To our knowledge, this model provides an unprecedented opportunity to study the complex biology and natural history of NF1 and could prove indispensable for development of imaging methods, biomarkers, and evaluation of safety and efficacy of NF1-targeted therapies

Bio-inspired approaches for critical infrastructure protection: Application of clonal selection principle for intrusion detection and FACTS placement

In this research, Clonal Selection, an immune system inspired approach, is utilized along with Evolutionary Algorithms to solve complex engineering problems such as Intrusion Detection and optimization of Flexible AC Transmission System (FACTS) device placement in a power grid. The clonal selection principle increases the strength of good solutions and alters their properties to find better solutions in a problem space. A special class of evolutionary algorithms that utilizes the clonal selection principle to guide its heuristic search process is termed Clonal EA. Clonal EAs can be used to solve complex pattern recognition and function optimization problems, which involve searching an enormous problem space for a solution. Intrusion Detection is modeled, in this research, as a pattern recognition problem wherein efficient detectors are to be designed to detect intrusive behavior. Optimization of FACTS device placement in a power grid is modeled as a function optimization problem wherein optimal placement positions for FACTS devices are to be determined, in order to balance load across power lines. Clonal EAs are designed to implement the solution models. The benefits and limitations of using Clonal EAs to solve the above mentioned problems are discussed and the performance of Clonal EAs is compared with that of traditional evolutionary algorithms and greedy algorithms --Abstract, page iii

The zinc finger transcription factor PLAGL2 enhances stem cell fate and activates expression of ASCL2 in intestinal epithelial cells

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