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Environmental, social, and governance accounting (ESGA) aids organizations in achieving their sustainability goals through continuous improvement. Suitable method selection is crucial to prevent rework, additional expenses, trivial outcomes, and reduced confidence in sustainability practices. The current ESGA method selection process lacks comprehensive consideration of alternatives and criteria, occasionally resulting in suboptimal choices. This work aims to achieve optimal ESGA by engineering a method selection framework. The research approach is based on the design cycle, where engineering decisions are informed by empirical evidence. The main findings are that the framework, which includes a decision model and a supporting information system, can reduce the chances of organizations selecting an unsuitable method, whilst sparing decision-making managers time and effort. Firstly, the reusable elements of the framework can help managers of any organization select a suitable method more efficiently since they do not have to produce these elements themselves. Secondly, the results demonstrate how selection frameworks and tools can aid organizations in navigating the complex ESG accounting landscape. Lastly, this study lowers the barrier for organizational impact management; in particular, for measuring and reporting ESG impact, which is a rigorous assessment of the organization’s progress towards sustainable development goals.

Business process modeling is a crucial aspect of domains such as Business Process Management and Software Engineering. The availability of various BPM languages in the market makes it challenging for process modelers to select the best-fit BPM language for a specific process modeling task. A decision model is necessary to systematically capture and make scattered knowledge on BPM languages available for reuse by process modelers and academics. This paper presents a decision model for the BPM language selection problem in research projects. The model contains mappings of 72 BPM features to 23 BPM languages. We validated and refined the decision model through 10 expert interviews with domain experts from various organizations. We evaluated the efficiency, validity, and generality of the decision model by conducting four case studies of academic research projects with their original researchers. The results confirmed that the decision model supports process modelers in the selection process by providing more insights into the decision process. Based on the empirical evidence from the case studies and domain expert feedback, we conclude that having the knowledge readily available in the decision model supports academics in making more informed decisions that align with their preferences and prioritized requirements. Furthermore, the captured knowledge provides a comprehensive overview of BPM languages, features, and quality characteristics that other researchers can employ to tackle future research challenges. Our observations indicate that BPMN is a commonly used modeling language for process modeling. Therefore, it is more sensible for academics to explain why they did not select BPMN than to discuss why they chose it for their research project(s).

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

Scientific research, increasingly reliant on data and computational analysis, confronts the challenge of integrating collaboration and data sharing across disciplines. Collaborative frameworks that support decentralized decision-making and knowledge-sharing are essential, yet integrating them into computational environments presents technical challenges, such as decentralized identity, user-centered policy-making, flexible asset management, automated provenance, and distributed collaborative workflow management. In this work, we propose a novel framework called Decentralized Virtual Research Environment (D-VRE) that addresses barriers to scientific research collaboration and data sharing, offering a scalable and adaptable decentralized model. It enhances seamless, trusted data sharing and collaboration within research lifecycles. It incorporates custom sharing policies, secure asset management, collaborative workflows, and research activity tracking, all without centralized oversight. Demonstrated through a real-world case study in the CLARIFY project, D-VRE proved effective in enabling advanced data sharing and collaborative scenarios, showcasing its adaptability in scientific research. Integrated into JupyterLab, D-VRE supports custom collaboration agreements and smart contract-based automated execution on the Ethereum blockchain. This ensures secure, verifiable transactions and promotes trust and reliability in shared research findings., Comment: This work has been submitted to the journal named Software: Practice and Experience

During the software selection process, software engineers often rely on text reviews from repository platforms, communities, or forums to collect software quality information. As these reviews offer direct insight into users' experience and perception of the software components. However, text reviews are often formulated implicitly, and the process of gathering user feedback from multiple sources can be a time-intensive endeavor, posing challenges in the collection and analysis of substantial volumes of data. We conducted a systematic literature review to explore the state-of-the-art solutions in sentiment analysis. By leveraging the knowledge derived from the literature review, we developed a sentiment analysis tool to measure software component quality by analyzing the sentiment of reviews from experienced users. Our goal is to provide a channel to help software stakeholders gain insight into the software quality attributes, thus enhancing the overall health of software and the software ecosystem. This tool consists of TextRank, which extracts keywords related to software quality attributes from raw data, an Aho-Corasick automaton used to search for these keywords in reviews and map them to software quality attributes, and a sentiment analysis model to perform sentiment analysis. We compare four widely mentioned models in the literature review, namely BERT, BERT-BiLSTM, BERT-BiLSTM-Attention, and RoBERTa, in terms of performance metrics such as accuracy, F1, precision, and recall. BERT-BiLSTM-Attention is selected as the sentiment analysis model due to its superior performance in both training and test datasets. In addition, we integrated a decision algorithm that computes the fuzzy group consensus sentiment for the relevant quality attributes of each software component and visualizes it through a sentiment quality matrix.

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.

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.

Searching and reusing source code play an increasingly significant role in the daily tasks of software developers. While code repositories, such as GitHub and Stackoverflow, may provide some results, a code search engine is generally considered most helpful when searching for code snippets as they typically crawl data from a wide range of code repositories. Code search engines enable software developers to search for code snippets using search terms. The accuracy of the search results can be increased if the searchers' intent can be modeled and predicted correctly. This study proposes a novel code search engine to model user intents through a dialogue system and then suggests a ranked list of code snippets that can meet user requirements.

Finding research software is a complex task, as research software engineers regularly search for algorithms and methods deeply embedded in large software packages during the creation of research software. Furthermore, they go through lengthy evaluation and extraction processes to find one particular algorithm relevant to their research project. Additionally, for creators of research software, it is hard to show the impact that their code has made on the field, and only very course measures exist for evaluating the success of research software. This study introduces the concept of FAIRSECO, which aims to enable research software engineers to rapidly find and extract relevant software fragments from the worldwide research software ecosystem. Research software engineers from all fields can transplant these source code fragments, maintain the provenance of source code, and easily credit the original authors of the software. Simultaneously, the platform also enables research software engineers to report on their software's impact. With FAIRSECO, we introduce a platform for research software engineers that creates a “method economy”, i.e., where smaller granularity reuse becomes possible while increasing FAIRness (Findable, Accessible, Interoperable, and Reusable) of the worldwide research software ecosystem.

Context: Software development is a continuous decision-making process that mainly relies on the software engineer's experience and intuition. One of the essential decisions in the early stages of the process is selecting the best fitting programming language ecosystem based on the project requirements. A significant number of criteria, such as developer availability and consistent documentation, in addition to the number of available options in the market, lead to a challenging decision-making process. As the selection of programming language ecosystems depends on the application to be developed and its environment, a decision model is required to analyze the selection problem using systematic identification and evaluation of potential alternatives for a development project. Method: Recently, we introduced a framework to build decision models for technology selection problems in software production. Furthermore, we designed and implemented a decision support system that uses such decision models to support software engineers with their decision-making problems. This study presents a decision model based on the framework for the programming language ecosystem selection problem. Results: The decision model has been evaluated through seven real-world case studies at seven software development companies. The case study participants declared that the approach provides significantly more insight into the programming language ecosystem selection process and decreases the decision-making process's time and cost. Conclusion: With the decision model, software engineers can more rapidly evaluate and select programming language ecosystems. Having the knowledge in the decision model readily available supports software engineers in making more efficient and effective decisions that meet their requirements and priorities. Furthermore, such reusable knowledge can be employed by other researchers to develop new concepts and solutions for future challenges.

Model-driven development platforms shift the focus of software development activity from coding to modeling for enterprises. A significant number of such platforms are available in the market. Selecting the best fitting platform is challenging, as domain experts are not typically model-driven deployment platform experts and have limited time for acquiring the needed knowledge. We model the problem as a multi-criteria decision-making problem and capture knowledge systematically about the features and qualities of 30 alternative platforms. Through four industry case studies, we confirm that the model supports decision-makers with the selection problem by reducing the time and cost of the decision-making process and by providing a richer list of options than the enterprises considered initially. We show that having decision knowledge readily available supports decision-makers in making more rational, efficient, and effective decisions. The study’s theoretical contribution is the observation that the decision framework provides a reliable approach for creating decision models in software production.

The software ecosystem is a trust-rich part of the world. Collaboratively, software engineers trust major hubs in the ecosystem, such as package managers, repository services, and programming language ecosystems. However, trust entails the assumption of risks. In this paper, we lay out the risks we are taking by blindly trusting these hubs when using information systems. Secondly, we present a vision for a trust-recording mechanism in the software ecosystem that mitigates the presented risks. This vision is realized in TrustSECO: a distributed infrastructure that collects, stores, and discloses trust facts about information systems. If our community manages to implement this mechanism, we can create an urgently needed healthy and secure software ecosystem. Finally, we report on the current status of the project.

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.

The selection process of architectural patterns is challenging for software architects, as knowledge about patterns is scattered among a wide range of literature. Knowledge about architectural patterns must be collected, organized, stored, and quickly retrieved when it needs to be employed. In this tool paper, we introduce a decision support system that uses a decision model for supporting software architects with the pattern selection problem according to their requirements, including functional and quality requirements. The decision model is built based on a technology selection framework for modeling multi-criteria decision-making problems in software production. Twenty-four software architects in the Netherlands have evaluated the tool. They confirm that the tool supports them with their daily decision-making process.

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, intending to build 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. 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.

Decision making is an inevitable part of software engineering. Software engineers make a considerable number of decisions during the software development life cycle. Thus, as a subset of software engineering, software production can be considered a continuous decision-making process. The decision process refers to the steps involved in choosing and evaluating the best fitting alternative solution(s) for software engineers, as decision-makers, according to their preferences and requirements. In order to make informed decisions, the decision-makers around a software product should either acquire knowledge themselves or hire external experts to support them with their decision-making process. The process gets more complicated as the number of decision-makers, alternatives, and criteria increases. Therefore, software production is a suitable domain to deploy decision support systems that intelligently support these decision-makers in the decision-making process. A decision model for each decision-making problem is required to externalize and organize knowledge regarding the selection context. In this dissertation, we focus on pragmatically selected decision-making problems that software engineers face in software production. The following categories of software production decisions are discussed: (1) decision-making regarding COTS components for inclusion into software products. (2) decision problems related to software development technologies that deal with finding the best fitting technologies for developing a software product. (3) architectural design decisions concerning pattern-driven software design. We developed a theoretical framework to assist software engineers with a set of Multi-Criteria Decision-Making (MCDM) problems in software production. The framework provides a guideline for software engineers to systematically capture knowledge from different knowledge sources to build decision models for MCDM problems in software production. The framework and the de

Blockchain technology has received significant attention recently, as it offers a reliable decentralized infrastructure for all kinds of business transactions. Software-producing organizations are increasingly considering blockchain technology for inclusion into their software products. Selecting the best fitting blockchain platform requires the assessment of its functionality, adaptability, and compatibility to the existing software product. Novice software developers and architects are not experts in every domain, so they should either consult external experts or acquire knowledge themselves. The decision-making process gets more complicated as the number of decision-makers, alternatives, and criteria increases. Hence, a decision model is required to externalize and organize knowledge regarding the blockchain platform selection context. Recently, we designed a decision support system to use such decision models to support decision-makers with their technology selection problems in software production. In this article, we introduce a decision model for the blockchain platform selection problem. The decision model has been evaluated through three real-world case studies at three software-producing organizations. The case-study participants asserted that the approach provides significantly more insight into the blockchain platform selection process, provides a richer prioritized option list than if they had done their research independently, and reduces the time and cost of the decision-making process.

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.