1,720,965 research outputs found
Beyond domain dependency in security requirements identification
No full textContext: Early security requirements identification is crucial in software development, facilitating the integration of security measures into IT networks and reducing time and costs throughout software life-cycle. Objectives: This paper addresses the limitations of existing methods that leverage Natural Language Processing (NLP) and machine learning techniques for detecting security requirements. These methods often fall short in capturing syntactic and semantic relationships, face challenges in adapting across domains, and rely heavily on extensive domain-specific data. In this paper we focus on identifying the most effective approaches for this task, highlighting both domain-specific and domain-independent strategies. Method: Our methodology encompasses two primary streams of investigation. First, we explore shallow machine learning techniques, leveraging word embeddings. We test ensemble methods and grid search within and across domains, evaluating on three industrial datasets. Next, we develop several domain-independent models based on BERT, tailored to better detect security requirements by incorporating data on software weaknesses and vulnerabilities. Results: Our findings reveal that ensemble and grid search methods prove effective in domain-specific and domain-independent experiments, respectively. However, our custom BERT models showcase domain independence and adaptability. Notably, the CweCveCodeBERT model excels in Precision and F1-score, outperforming existing approaches significantly. It improves F1-score by ∼3% and Precision by ∼14% over the best approach currently in the literature. Conclusion: BERT-based models, especially with specialized pre-training, show promise for automating security requirement detection. This establishes a foundation for software engineering researchers and practitioners to utilize advanced NLP to improve security in early development phases, fostering the adoption of these state-of-the-art methods in real-world scenarios
PReDUS: A Privacy Requirements Detector From User Stories
No full textIn the context of requirements engineering, stakeholders are often unaware of identifying and managing privacy and security requirements. The purpose of this paper is to present a tool, namely PReDUS, for the detection of privacy content from user stories. The core of the tool is the use of deep learning algorithms that exploit Natural Language Processing techniques and linguistic resources
Detecting privacy requirements from User Stories with NLP transfer learning models
No full textContext: To provide privacy-aware software systems, it is crucial to consider privacy from the very beginning of the development. However, developers do not have the expertise and the knowledge required to embed the legal and social requirements for data protection into software systems. Objective: We present an approach to decrease privacy risks during agile software development by automatically detecting privacy-related information in the context of user story requirements, a prominent notation in agile Requirement Engineering (RE). Methods: The proposed approach combines Natural Language Processing (NLP) and linguistic resources with deep learning algorithms to identify privacy aspects into User Stories. NLP technologies are used to extract information regarding the semantic and syntactic structure of the text. This information is then processed by a pre-trained convolutional neural network, which paved the way for the implementation of a Transfer Learning technique. We evaluate the proposed approach by performing an empirical study with a dataset of 1680 user stories. Results: The experimental results show that deep learning algorithms allow to obtain better predictions than those achieved with conventional (shallow) machine learning methods. Moreover, the application of Transfer Learning allows to considerably improve the accuracy of the predictions, ca. 10%. Conclusions: Our study contributes to encourage software engineering researchers in considering the opportunities to automate privacy detection in the early phase of design, by also exploiting transfer learning models
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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