Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Structured Review of Code Clone Literature

This report presents the results of a structured review of code clone literature. The aim of the review is to assemble a conceptual model of clone-related concepts which helps us to reason about clones. This conceptual model unifies clone concepts from a wide range of literature, so that findings about clones can be compared with each other

The IsomiR Window: the interface that bridges the complexity of miRNAs and their functional impact

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2017Os métodos de sequenciação de elevado débito, conhecidos como Next-Generation Sequencing (NGS), têm sido bastante usados nos últimos anos, permitindo obter, em paralelo, milhões de sequências de DNA ou de RNA. Estes métodos são muito aplicados no estudo de moléculas de RNA de pequenas dimensões, nas quais se incluem os microRNAs (miRNAs), sendo estes conhecidos como reguladores da expressão génica. Adicionalmente, estes métodos permitiram a descoberta de variantes dos miRNAs que exibem alterações na sua sequência. Estas variantes denominam-se isomiRs, podendo pertencer a três grupos: isomiRs 5’, isomiRs 3’, e isomiRs com mudanças internas. Atualmente existem várias ferramentas de bioinformática que permitem a identificação sistemática de isomiRs. No entanto, apesar dos esforços destas ferramentas em fornecer plataformas computacionais especializadas para a análise de dados de sequenciação de RNAs de pequenas dimensões, estas têm em falta bastantes funcionalidades, não permitindo que o investigador receba todo o contexto dos dados, e, por consequência, a complexidade dos isomiRs na amostra não é devidamente explorada. Uma funcionalidade que está em falta nestas ferramentas é a possibilidade de o utilizador realizar de forma integrada a análise de anotação de sequências, incluindo a expressão diferencial, e a análise de impacto funcional dos isomiRs encontrados nas amostras. Outro aspeto importante é a maioria destas ferramentas não permitir analisar dados de NGS. As que permitem analisar estes dados, não permitem a análise em paralelo de vários ficheiros e apresentam limites de tamanho demasiado reduzidos para os ficheiros de dados NGS. Adicionalmente, muitas das ferramentas não disponibilizam uma interface gráfica, tornando a tarefa de analisar dados de sequenciação mais difícil para investigadores que não têm conhecimentos em bioinformática. Desta forma, é importante a existência de uma ferramenta que integre todas as análises necessárias, nomeadamente a identificação de isomiRs num conjunto de dados, assim como a inferência do impacto funcional destas moléculas, e que possua uma interface gráfica fácil de usar. Assim, este projeto teve como objetivo contribuir para o desenvolvimento de uma ferramenta que permita a identificação rápida e eficiente de isomiRs e que integre diferentes funcionalidades de um modo automático, que vão desde a anotação de pequenos RNAs em dados de NGS à análise funcional para investigar o impacto biológico dos isomiRs identificados. Como contribuição principal deste projeto foi criada uma aplicação web, que integra uma pipeline de bioinformática (fora do âmbito desta tese), e que suporta dois módulos de análise, de anotação e funcional, tendo sido considerada de raiz a transferência de informação entre os dois módulos de análise. Esta aplicação tem um conjunto mais completo de funcionalidades do que outras ferramentas existentes, apenas precisando de um browser web para poder ser usada. O funcionamento da aplicação foi testado utilizando dados de NGS disponíveis publicamente, tendo demonstrado a capacidade desta para processar vários ficheiros de uma forma integrada, produzindo gráficos e tabelas que demonstram os resultados deste processamento. Estes revelam uma complexidade das moléculas de pequenos RNAs não codificantes que não tinha sido previamente observada. Finalmente, foi criada uma máquina virtual com a aplicação desenvolvida, assim como todo o software da qual esta depende, de um modo pronto a usar, a qual está disponível no endereço http://isomir.fc.ul.pt.Next-Generation Sequencing (NGS) methods have been widely used over the past years, allowing researchers to obtain, in parallel, millions of DNA and RNA sequences. These methods are extensively applied in the study of small RNA molecules, in which microRNAs (miRNAs) are included, which are known to act as regulators of gene expression. Additionally, NGS methods have permitted the discovery of variants of miRNAs, which exhibit changes in their sequence when compared to the canonical miRNA, and are called isomiRs. These molecules belong to one of three groups: 5’ isomiRs, 3’ isomiRs, and isomiRs with internal editings. Nowadays, there are several bioinformatics tools that allow the systematic identification of isomiRs. However, they lack several key functionalities that prevent the user from understanding the entire complexity within the data, and consequently, the complexity of the isomiRs is not fully explored. One functionality that is absent in these tools, is an integrated workflow to sequentially, annotate sequences, infer differential expression, and assess the functional impact of isomiRs. Importantly, many of these tools do not accept NGS data as input. Regarding the ones that accept NGS data, they do not allow the analysis of several files in parallel and limit the size of the input in a way that excludes many NGS files. Furthermore, the lack of a graphical interface in these tools is also common, making the task of analyzing NGS data harder for researchers that are not familiar with bioinformatics concepts. Thus, it is important to have a tool that integrates all the required analysis for isomiR identification and for inferring the functional impacts of those molecules, and that provides an easy to use graphical interface. Therefore, the main goal of this project was the development of a tool that allows a quick and efficient identification of isomiRs and that integrates different functionalities automatically, including the annotation of small non-coding RNAs in NGS data and the functional analysis so that the researcher can investigate the biological impact of the identified isomiRs. The main contribution of this project was the development of a web application, which integrates a bioinformatics pipeline (outside the scope of this thesis), that allows the execution of two types of analyses, annotation and functional, having been built from scratch to support the sharing of data between the two analyses. This application presents a more complete set of functionalities, compared to other existing tools, and is available to the user through a web browser. The tool benchmarking was performed using publicly available NGS data, showing the ability to process multiple datasets in an integrated manner and producing reports of results in charts and table displays. These results show the complexity of small non-coding RNAs that had not been explored in the study. A virtual machine was created, in which the web application and pipeline are installed and configured as well as third-party software dependencies. The virtual machine is ready to use and it is available at http://isomir.fc.ul.pt

On Different Approaches to Syntactic Analysis Into Bi-Lexical Dependencies. An Empirical Comparison of Direct, PCFG-Based, and HPSG-Based Parsers

We compare three different approaches to parsing into syntactic, bi-lexical dependencies for English: a ‘direct’ data-driven dependency parser, a statistical phrase structure parser, and a hybrid, ‘deep’ grammar-driven parser. The analyses from the latter two are post-converted to bilexical dependencies. Through this ‘reduction’ of all three approaches to syntactic dependency parsers, we determine empirically what performance can be obtained for a common set of dependency types for English, across a broad variety of domains. In doing so, we observe what trade-offs apply along three dimensions, accuracy, efficiency, and resilience to domain variation. Our results suggest that the hand-built grammar in one of our parsers helps in both accuracy and cross-domain performance. Proceedings of The 13th International Conference on Parsing Technologies IWPT-2013

Effectiveness of Linux rootkit detection tools

Abstract. Rootkits — a type of software that specializes in hiding entities in computer systems while enabling continuous control or access to it — are particularly difficult to detect compared to other kinds of software. Various tools exist for detecting rootkits, utilizing a wide variety of detection techniques and mechanisms. However, the effectiveness of such tools is not well established, especially in contemporary academic research and in the context of the Linux operating system. This study carried out an empirical evaluation of the effectiveness of five tools with capabilities to detect Linux rootkits: OSSEC, AIDE, Rootkit Hunter, Chkrootkit and LKRG. The effectiveness of each tool was tested by injecting 15 publicly available rootkits in individual detection tests in virtual machines running Ubuntu 16.04, executing the detection tool and capturing its results for analysis. A total of 75 detection tests were performed. The results showed that only 37.3% of the detection tests provided any indication of a rootkit infection or suspicious system behaviour, with the rest failing to provide any signs of anomalous behaviour. However, combining the findings of multiple detection tools increased the overall detection rate to 93.3%, as all but a single rootkit were discovered by at least one tool. Variation was observed in the effectiveness of the detection tools, with detection rates ranging from 13.3% to 53.3%. Variation in detection effectiveness was also found between categories of rootkits, as the overall detection rate was 46.7% for user mode rootkits and 31.1% for kernel mode rootkits. Overall, the findings showed that while an individual detection tool‘s effectiveness can be lacking, using a combination of tools considerably increased the likelihood of a successful detection