Clinical software development for the Web: lessons learned from the BOADICEA project.

BACKGROUND: In the past 20 years, society has witnessed the following landmark scientific advances: (i) the sequencing of the human genome, (ii) the distribution of software by the open source movement, and (iii) the invention of the World Wide Web. Together, these advances have provided a new impetus for clinical software development: developers now translate the products of human genomic research into clinical software tools; they use open-source programs to build them; and they use the Web to deliver them. Whilst this open-source component-based approach has undoubtedly made clinical software development easier, clinical software projects are still hampered by problems that traditionally accompany the software process. This study describes the development of the BOADICEA Web Application, a computer program used by clinical geneticists to assess risks to patients with a family history of breast and ovarian cancer. The key challenge of the BOADICEA Web Application project was to deliver a program that was safe, secure and easy for healthcare professionals to use. We focus on the software process, problems faced, and lessons learned. Our key objectives are: (i) to highlight key clinical software development issues; (ii) to demonstrate how software engineering tools and techniques can facilitate clinical software development for the benefit of individuals who lack software engineering expertise; and (iii) to provide a clinical software development case report that can be used as a basis for discussion at the start of future projects. RESULTS: We developed the BOADICEA Web Application using an evolutionary software process. Our approach to Web implementation was conservative and we used conventional software engineering tools and techniques. The principal software development activities were: requirements, design, implementation, testing, documentation and maintenance. The BOADICEA Web Application has now been widely adopted by clinical geneticists and researchers. BOADICEA Web Application version 1 was released for general use in November 2007. By May 2010, we had > 1200 registered users based in the UK, USA, Canada, South America, Europe, Africa, Middle East, SE Asia, Australia and New Zealand. CONCLUSIONS: We found that an evolutionary software process was effective when we developed the BOADICEA Web Application. The key clinical software development issues identified during the BOADICEA Web Application project were: software reliability, Web security, clinical data protection and user feedback.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Trusted CI Experiences in Cybersecurity and Service to Open Science

This article describes experiences and lessons learned from the Trusted CI project, funded by the US National Science Foundation to serve the community as the NSF Cybersecurity Center of Excellence. Trusted CI is an effort to address cybersecurity for the open science community through a single organization that provides leadership, training, consulting, and knowledge to that community. The article describes the experiences and lessons learned of Trusted CI regarding both cybersecurity for open science and managing the process of providing centralized services to a broad and diverse community.Comment: 8 pages, PEARC '19: Practice and Experience in Advanced Research Computing, July 28-August 1, 2019, Chicago, IL, US

Bridging the gap between research and agile practice: an evolutionary model

There is wide acceptance in the software engineering field that industry and research can gain significantly from each other and there have been several initiatives to encourage collaboration between the two. However there are some often-quoted challenges in this kind of collaboration. For example, that the timescales of research and practice are incompatible, that research is not seen as relevant for practice, and that research demands a different kind of rigour than practice supports. These are complex challenges that are not always easy to overcome. Since the beginning of 2013 we have been using an approach designed to address some of these challenges and to bridge the gap between research and practice, specifically in the agile software development arena. So far we have collaborated successfully with three partners and have investigated three practitioner-driven challenges with agile. The model of collaboration that we adopted has evolved with the lessons learned in the first two collaborations and been modified for the third. In this paper we introduce the collaboration model, discuss how it addresses the collaboration challenges between research and practice and how it has evolved, and describe the lessons learned from our experience

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Knowledge re-use for decision support

Effective decision support has already been identified as a fundamental requirement for the realisation of Network Enabled Capability. Decision making itself is a knowledge-intensive process, and it is known that right decisions can only be reached based on decision maker's good judgement, which in turn is based on sufficient knowledge. It is not unusual for decision makers to make incorrect decisions because of insufficient knowledge. However, it is not always possible for decision makers to have all the knowledge needed for making decisions in complex situations without external support. The re-use of knowledge has been identified as providing an important contribution to such support, and this paper considers one, hitherto unexplored, aspect of how this may be achieved. This paper is concerned with the computational view of knowledge re-use to establish an understanding of a knowledge-based system for decision support. The paper explores knowledge re-use for decision support from two perspectives: knowledge provider's and knowledge re-user's. Key issues and challenges of knowledge re-use are identified from both perspectives. A structural model for knowledge re-use is proposed with initial evaluation through empirical study of both experienced and novice decision maker's behaviour in reusing knowledge to make decisions. The proposed structural model for knowledge re-use captures five main elements (knowledge re-uers, knowledge types, knowledge sources, environment, and integration strategies) as well as the relationships between the elements, which forms a foundation for constructing a knowledge-based decision support system. The paper suggests that further research should be investigating the relationship between knowledge re-use and learning to achieve intelligent decision support

Summary of the First Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE1)

Challenges related to development, deployment, and maintenance of reusable software for science are becoming a growing concern. Many scientists’ research increasingly depends on the quality and availability of software upon which their works are built. To highlight some of these issues and share experiences, the First Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE1) was held in November 2013 in conjunction with the SC13 Conference. The workshop featured keynote presentations and a large number (54) of solicited extended abstracts that were grouped into three themes and presented via panels. A set of collaborative notes of the presentations and discussion was taken during the workshop. Unique perspectives were captured about issues such as comprehensive documentation, development and deployment practices, software licenses and career paths for developers. Attribution systems that account for evidence of software contribution and impact were also discussed. These include mechanisms such as Digital Object Identifiers, publication of “software papers”, and the use of online systems, for example source code repositories like GitHub. This paper summarizes the issues and shared experiences that were discussed, including cross-cutting issues and use cases. It joins a nascent literature seeking to understand what drives software work in science, and how it is impacted by the reward systems of science. These incentives can determine the extent to which developers are motivated to build software for the long-term, for the use of others, and whether to work collaboratively or separately. It also explores community building, leadership, and dynamics in relation to successful scientific software

Lessons learned in developing a Second Life educational environment

Virtual worlds are rapidly spreading beyond gaming and entertainment into education and the corporate world. Should this trend continue, as forecast by the industry, then immersive applications will become more prominent, with bespoke software developed in the metaverse affording both opportunities and challenges. This paper reflects on the experience of developing a learning virtual space based on Second Life as part of an innovation project at The Open University, UK. The paper focuses on the lessons learnt from the viewpoint of managing the development of the learning environment, and could be of benefit to educators and educational technologists who are thinking to engage in this sort of development