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We conducted a thorough SLR to better grasp the challenges and possible solutions associated with existing npm security tools. Our goal was to delve into documented experiences and findings. Specifically, we were keen to learn about the motivations behind choosing third-party packages, software engineers' responses to warning messages, and their overall understanding of security issues. The main aim of this review was to pinpoint prevailing trends, methods, and concerns in trust tools for the present npm environment. Furthermore, we sought to understand the complexities of integrating SECO into platforms such as npm. By analyzing earlier studies, our intention was to spot any overlooked areas and steer our research to address them.

The growing usage of research software in the research community has highlighted the need to recognize and acknowledge the contributions made not only by researchers but also by Research Software Engineers. However, the existing methods for crediting research software and Research Software Engineers have proven to be insufficient. In response, we have developed FAIRSECO, an extensible open source framework with the objective of assessing the impact of research software in research through the evaluation of various factors. The FAIRSECO framework addresses two critical information needs: firstly, it provides potential users of research software with metrics related to software quality and FAIRness. Secondly, the framework provides information for those who wish to measure the success of a project by offering impact data. By exploring the quality and impact of research software, our aim is to ensure that Research Software Engineers receive the recognition they deserve for their valuable contributions., Comment: 10 pages, 4 figures 2 tables. Preprint of the IEEE eScience 2023 Conference publication

Today, scientific research is increasingly data-centric and compute-intensive, relying on data and models across distributed sources. However, it still faces challenges in the traditional cooperation mode, due to the high storage and computing cost, geo-location barriers, and local confidentiality regulations. The Jupyter environment has recently emerged and evolved as a vital virtual research environment for scientific computing, which researchers can use to scale computational analyses up to larger datasets and high-performance computing resources. Nevertheless, existing approaches lack robust support of a decentralized cooperation mode to unlock the full potential of decentralized collaborative scientific research, e.g., seamlessly secure data sharing. In this work, we change the basic structure and legacy norms of current research environments via the seamless integration of Jupyter with Ethereum blockchain capabilities. As such, it creates a Decentralized Virtual Research Environment (D-VRE) from private computational notebooks to decentralized collaborative research ecosystem. We propose a novel architecture for the D-VRE and prototype some essential D-VRE elements for enabling secure data sharing with decentralized identity, user-centric agreement-making, membership, and research asset management. To validate our method, we conducted an experimental study to test all functionalities of D-VRE smart contracts and their gas consumption. In addition, we deployed the D-VRE prototype on a test net of the Ethereum blockchain for demonstration. The feedback from the studies showcases the current prototype's usability, ease of use, and potential and suggests further improvements., Comment: We revised the manuscript draft and submitted the revised manuscript to the journal Blockchain: Research and Applications

Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user's request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.

Virtual Research Environments (VREs) provide user-centric support in the lifecycle of research activities, e.g., discovering and accessing research assets, or composing and executing application workflows. A typical VRE is often implemented as an integrated environment, which includes a catalog of research assets, a workflow management system, a data management framework, and tools for enabling collaboration among users. Notebook environments, such as Jupyter, allow researchers to rapidly prototype scientific code and share their experiments as online accessible notebooks. Jupyter can support several popular languages that are used by data scientists, such as Python, R, and Julia. However, such notebook environments do not have seamless support for running heavy computations on remote infrastructure or finding and accessing software code inside notebooks. This paper investigates the gap between a notebook environment and a VRE and proposes an embedded VRE solution for the Jupyter environment called Notebook-as-a-VRE (NaaVRE). The NaaVRE solution provides functional components via a component marketplace and allows users to create a customized VRE on top of the Jupyter environment. From the VRE, a user can search research assets (data, software, and algorithms), compose workflows, manage the lifecycle of an experiment, and share the results among users in the community. We demonstrate how such a solution can enhance a legacy workflow that uses Light Detection and Ranging (LiDAR) data from country-wide airborne laser scanning surveys for deriving geospatial data products of ecosystem structure at high resolution over broad spatial extents. This enables users to scale out the processing of multi-terabyte LiDAR point clouds for ecological applications to more data sources in a distributed cloud environment., Comment: A revised version has been published in the journal software practice and experience

Decentralized autonomous organizations as a new form of online governance arecollections of smart contracts deployed on a blockchain platform that intercede groupsof people. A growing number of Decentralized Autonomous Organization Platforms,such as Aragon and Colony, have been introduced in the market to facilitate thedevelopment process of such organizations. Selecting the best fitting platform ischallenging for the organizations, as a significant number of decision criteria, such aspopularity, developer availability, governance issues, and consistent documentation ofsuch platforms, should be considered. Additionally, decision-makers at theorganizations are not experts in every domain, so they must continuously acquirevolatile knowledge regarding such platforms and keep themselves updated.Accordingly, a decision model is required to analyze the decision criteria usingsystematic identification and evaluation of potential alternative solutions for adevelopment project. We have developed a theoretical framework to assist softwareengineers with a set of Multi-Criteria Decision-Making problems in software production.This study presents a decision model as a Multi-Criteria Decision-Making problem forthe decentralized autonomous organization platform selection problem. Weconducted three industry case studies in the context of three decentralizedautonomous organizations to evaluate the effectiveness and efficiency of the decisionmodel in assisting decision-makers.

Context: Software architecture is a knowledge-intensive field. One mechanism for storing architecture knowledge is the recognition and description of architectural patterns. Selecting architectural patterns is a challenging task for software architects, as knowledge about these patterns is scattered among a wide range of literature. Method: We report on a systematic literature review, with the aim of building a decision model for the architectural pattern selection problem. Moreover, twelve experienced practitioners at software-producing organizations evaluated the usability and usefulness of the extracted knowledge.\newline Results: An overview is provided of 29 patterns and their effects on 40 quality attributes. Furthermore, we report in which systems the 29 patterns are applied and in which combinations. The practitioners confirmed that architectural knowledge supports software architects with their decision-making process to select a set of patterns for a new problem. We investigate the potential trends among architects to select patterns. Conclusion: With the knowledge available, architects can more rapidly select and eliminate combinations of patterns to design solutions. Having this knowledge readily available supports software architects in making more efficient and effective design decisions that meet their quality concerns.