Collaborating with the Crowd for Software Requirements Engineering: A Literature Review

Requirements engineering (RE) represents a decisive success factor in software development. The novel approach of crowd-based RE seeks to overcome shortcomings of traditional RE practices such as the resource intensiveness and selection bias of stakeholder workshops or interviews. Two streams of research on crowd-based RE can be observed in literature: data-driven approaches that extract requirements from user feedback or analytics data and collaborative approaches in which requirements are collectively developed by a crowd of software users. As yet, research surveying the state of crowd-based RE does not put particular emphasis on collaborative approaches, despite collaborative crowdsourcing being particularly suited for joint ideation and complex problem-solving tasks. Addressing this gap, we conduct a structured literature review to identify the RE activities supported by collaborative crowd-based approaches. Our research provides a systematic overview of the domain of collaborative crowd-based RE and guides researchers and practitioners in increasing user involvement in RE

Decision Support in Knowledge Acquisition: Concept Characterization Using Genetic Algorithms

We demonstrate the use of an unsupervised learning technique called genetic algorithms to discover the association between a concept and its key attributes in concept characterization. The resulting concept- attribute associations are important domain concepts for knowledge engineers to structure interviews with the experts or to prepare representative data for inductive inference. Examples based on the part family identification problem in manufacturing are employed to illustrate the identification capability of our technique. Preliminary results from testing the technique in a SUN SPARC station 1+ indicate that it can be exploited as a decision support tool to assist knowledge engineers in the conceptualization stage of the knowledge acquisition process

SRPTackle: A semi-automated requirements prioritisation technique for scalable requirements of software system projects

ContextRequirement prioritisation (RP) is often used to select the most important system requirements as perceived by system stakeholders. RP plays a vital role in ensuring the development of a quality system with defined constraints. However, a closer look at existing RP techniques reveals that these techniques suffer from some key challenges, such as scalability, lack of quantification, insufficient prioritisation of participating stakeholders, overreliance on the participation of professional expertise, lack of automation and excessive time consumption. These key challenges serve as the motivation for the present research.ObjectiveThis study aims to propose a new semiautomated scalable prioritisation technique called ‘SRPTackle’ to address the key challenges.MethodSRPTackle provides a semiautomated process based on a combination of a constructed requirement priority value formulation function using a multi-criteria decision-making method (i.e. weighted sum model), clustering algorithms (K-means and K-means++) and a binary search tree to minimise the need for expert involvement and increase efficiency. The effectiveness of SRPTackle is assessed by conducting seven experiments using a benchmark dataset from a large actual software project.ResultsExperiment results reveal that SRPTackle can obtain 93.0% and 94.65% as minimum and maximum accuracy percentages, respectively. These values are better than those of alternative techniques. The findings also demonstrate the capability of SRPTackle to prioritise large-scale requirements with reduced time consumption and its effectiveness in addressing the key challenges in comparison with other techniques.ConclusionWith the time effectiveness, ability to scale well with numerous requirements, automation and clear implementation guidelines of SRPTackle, project managers can perform RP for large-scale requirements in a proper manner, without necessitating an extensive amount of effort (e.g. tedious manual processes, need for the involvement of experts and time workload)

Clinical text data in machine learning: Systematic review

Background: Clinical narratives represent the main form of communication within healthcare providing a personalized account of patient history and assessments, offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study is to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigate the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multi-faceted interface, to perform a literature search against MEDLINE. We identified a total of 110 relevant studies and extracted information about the text data used to support machine learning, the NLP tasks supported and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation and any relevant statistics. Results: The vast majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable due to sensitive nature of data considered. Beside the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The vast majority of studies focused on the task of text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management and surveillance. Conclusions: We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which does not require data annotation

Software Development Process Modeling. Developers Perspective to Contemporary Modeling Techniques

Formal software development processes and well-defined development methodologies are nowadays seen as the definite way to produce high-quality software within time-limits and budgets. The variety of such high-level methodologies is huge ranging from rigorous process frameworks like CMMI and RUP to more lightweight agile methodologies. The need for managing this variety and the fact that practically every software development organization has its own unique set of development processes and methods have created a profession of software process engineers. Different kinds of informal and formal software process modeling languages are essential tools for process engineers. These are used to define processes in a way which allows easy management of processes, for example process dissemination, process tailoring and process enactment. The process modeling languages are usually used as a tool for process engineering where the main focus is on the processes themselves. This dissertation has a different emphasis. The dissertation analyses modern software development process modeling from the software developers’ point of view. The goal of the dissertation is to investigate whether the software process modeling and the software process models aid software developers in their day-to-day work and what are the main mechanisms for this. The focus of the work is on the Software Process Engineering Metamodel (SPEM) framework which is currently one of the most influential process modeling notations in software engineering. The research theme is elaborated through six scientific articles which represent the dissertation research done with process modeling during an approximately five year period. The research follows the classical engineering research discipline where the current situation is analyzed, a potentially better solution is developed and finally its implications are analyzed. The research applies a variety of different research techniques ranging from literature surveys to qualitative studies done amongst software practitioners. The key finding of the dissertation is that software process modeling notations and techniques are usually developed in process engineering terms. As a consequence the connection between the process models and actual development work is loose. In addition, the modeling standards like SPEM are partially incomplete when it comes to pragmatic process modeling needs, like light-weight modeling and combining pre-defined process components. This leads to a situation, where the full potential of process modeling techniques for aiding the daily development activities can not be achieved. Despite these difficulties the dissertation shows that it is possible to use modeling standards like SPEM to aid software developers in their work. The dissertation presents a light-weight modeling technique, which software development teams can use to quickly analyze their work practices in a more objective manner. The dissertation also shows how process modeling can be used to more easily compare different software development situations and to analyze their differences in a systematic way. Models also help to share this knowledge with others. A qualitative study done amongst Finnish software practitioners verifies the conclusions of other studies in the dissertation. Although processes and development methodologies are seen as an essential part of software development, the process modeling techniques are rarely used during the daily development work. However, the potential of these techniques intrigues the practitioners. As a conclusion the dissertation shows that process modeling techniques, most commonly used as tools for process engineers, can also be used as tools for organizing the daily software development work. This work presents theoretical solutions for bringing the process modeling closer to the ground-level software development activities. These theories are proven feasible by presenting several case studies where the modeling techniques are used e.g. to find differences in the work methods of the members of a software team and to share the process knowledge to a wider audience.Siirretty Doriast

Handling uncertainty in models of seismic and postseismic hazards: toward robust methods and resilient societies

Earthquakes, tsunamis, and landslides take a devastating toll on human lives, critical infrastructure, and ecosystems. Harnessing the predictive capacities of hazard models is key to transitioning from reactive approaches to disaster management toward building resilient societies, yet the knowledge that these models produce involves multiple uncertainties. The failure to properly account for these uncertainties has at times had important implications, from the flawed safety measures at the Fukushima power plant, to the reliance on short‐term earthquake prediction models (reportedly at the expense of mitigation efforts) in modern China. This article provides an overview of methods for handling uncertainty in probabilistic seismic hazard assessment, tsunami hazard analysis, and debris flow modeling, considering best practices and areas for improvement. It covers sensitivity analysis, structured approaches to expert elicitation, methods for characterizing structural uncertainty (e.g., ensembles and logic trees), and the value of formal decision‐analytic frameworks even in situations of deep uncertainty

System Security Assurance: A Systematic Literature Review

System security assurance provides the confidence that security features, practices, procedures, and architecture of software systems mediate and enforce the security policy and are resilient against security failure and attacks. Alongside the significant benefits of security assurance, the evolution of new information and communication technology (ICT) introduces new challenges regarding information protection. Security assurance methods based on the traditional tools, techniques, and procedures may fail to account new challenges due to poor requirement specifications, static nature, and poor development processes. The common criteria (CC) commonly used for security evaluation and certification process also comes with many limitations and challenges. In this paper, extensive efforts have been made to study the state-of-the-art, limitations and future research directions for security assurance of the ICT and cyber-physical systems (CPS) in a wide range of domains. We conducted a systematic review of requirements, processes, and activities involved in system security assurance including security requirements, security metrics, system and environments and assurance methods. We highlighted the challenges and gaps that have been identified by the existing literature related to system security assurance and corresponding solutions. Finally, we discussed the limitations of the present methods and future research directions

Building information modelling (BIM) to enhance occupational safety in construction activities. Research trends emerging from one decade of studies

In recent years, the use of new technologies is rapidly transforming the way working activities are managed and carried out. In the construction industry, in particular, the use of Building Information Modelling (BIM) is ever increasing as a means to improve the performances of numerous activities. In such a context, several studies have proposed BIM as a key process to augment occupational safety effectively, considering that the construction industry still remains one of the most hazardous working sectors. The purpose of the present study is to investigate the recent research addressing the use of BIM to improve construction safety. A systematic review was performed considering journal papers that appeared in literature in the last decade. The results showed that the most viable and promising research directions concern knowledge-based solutions, design for safety improvement through BIM solutions, transversal applications of BIM, and dynamic visualization and feedback. The findings of this study also indicated that more practical BIM applications are needed, especially focusing on safety training and education, the use of BIM to augment safety climate and resilience, and the development of quantitative risk analysis to better support safety management. Overall, the study provided a comprehensive research synthesis augmenting knowledge on the role of BIM-based tools in construction safety, which can be considered a reference framework to enhance workers' safety by means of these new technologies

Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions)

Background: Multiple long-term health conditions (multimorbidity) (MLTC-M) are increasingly prevalent and associated with high rates of morbidity, mortality, and health care expenditure. Strategies to address this have primarily focused on the biological aspects of disease, but MLTC-M also result from and are associated with additional psychosocial, economic, and environmental barriers. A shift toward more personalized, holistic, and integrated care could be effective. This could be made more efficient by identifying groups of populations based on their health and social needs. In turn, these will contribute to evidence-based solutions supporting delivery of interventions tailored to address the needs pertinent to each cluster. Evidence is needed on how to generate clusters based on health and social needs and quantify the impact of clusters on long-term health and costs. Objective: We intend to develop and validate population clusters that consider determinants of health and social care needs for people with MLTC-M using data-driven machine learning (ML) methods compared to expert-driven approaches within primary care national databases, followed by evaluation of cluster trajectories and their association with health outcomes and costs. Methods: The mixed methods program of work with parallel work streams include the following: (1) qualitative semistructured interview studies exploring patient, caregiver, and professional views on clinical and socioeconomic factors influencing experiences of living with or seeking care in MLTC-M; (2) modified Delphi with relevant stakeholders to generate variables on health and social (wider) determinants and to examine the feasibility of including these variables within existing primary care databases; and (3) cohort study with expert-driven segmentation, alongside data-driven algorithms. Outputs will be compared, clusters characterized, and trajectories over time examined to quantify associations with mortality, additional long-term conditions, worsening frailty, disease severity, and 10-year health and social care costs. Results: The study will commence in October 2021 and is expected to be completed by October 2023. Conclusions: By studying MLTC-M clusters, we will assess how more personalized care can be developed, how accurate costs can be provided, and how to better understand the personal and medical profiles and environment of individuals within each cluster. Integrated care that considers “whole persons” and their environment is essential in addressing the complex, diverse, and individual needs of people living with MLTC-M

Underpinning Quality Assurance: Identifying Core Testing Strategies for Multiple Layers of Internet-of-Things-Based Applications

The Internet of Things (IoT) constitutes a digitally integrated network of intelligent devices equipped with sensors, software, and communication capabilities, facilitating data exchange among a multitude of digital systems via the Internet. Despite its pivotal role in the software development life-cycle (SDLC) for ensuring software quality in terms of both functional and non-functional aspects, testing within this intricate software–hardware ecosystem has been somewhat overlooked. To address this, various testing techniques are applied for real-time minimization of failure rates in IoT applications. However, the execution of a comprehensive test suite for specific IoT software remains a complex undertaking. This paper proposes a holistic framework aimed at aiding quality assurance engineers in delineating essential testing methods across different testing levels within the IoT. This delineation is crucial for effective quality assurance, ultimately reducing failure rates in real-time scenarios. Furthermore, the paper offers a mapping of these identified tests to each layer within the layered framework of the IoT. This comprehensive approach seeks to enhance the reliability and performance of IoT-based applications