Abstract The appetite for effective use of information assets has been steadily rising in both public and private sector organisations. However, whether the information is used for social good or commercial gain, there is a growing recognition of the complex socio-technical challenges associated with balancing the diverse demands of regulatory compliance and data privacy, social expectations and ethical use, business process agility and value creation, and scarcity of data science talent. In this vision paper, we present a series of case studies that highlight these interconnected challenges, across a range of application areas. We use the insights from the case studies to introduce Information Resilience, as a scaffold within which the competing requirements of responsible and agile approaches to information use can be positioned. The aim of this paper is to develop and present a manifesto for Information Resilience that can serve as a reference for future research and development in relevant areas of responsible data management.

Grants are the steam engine of the research enterprise. Each year, billions of dollars from public and philanthropic organisations enable research projects across the world. However, despite significant investment in research and innovation, the information about grants and their impact is hidden behind disconnected, undiscoverable and inaccessible information systems. In this paper we present recent developments and discuss the need for a coordinated effort toward enabling open access to grant information. The paper also proposes a framework for coordinating technical and policy-making efforts toward linking grants to grant outcomes (publications, patents, research data, etc.). Such a framework can be seen as the institutional side of meta-research innovation.

SUMMARY Software dependences play a vital role in programme comprehension, change impact analysis and other software maintenance activities. Traditionally, these activities are supported by source code analysis; however, the source code is sometimes inaccessible or difficult to analyse, as in hybrid systems composed of source code in multiple languages using various paradigms (e.g. object‐oriented programming and relational databases). Moreover, not all stakeholders have adequate knowledge to perform such analyses. For example, non‐technical domain experts and consultants raise most maintenance requests; however, they cannot predict the cost and impact of the requested changes without the support of the developers. We propose a novel approach to predicting software dependences by exploiting the coupling present in domain‐level information. Our approach is independent of the software implementation; hence, it can be used to approximate architectural dependences without access to the source code or the database. As such, it can be applied to hybrid systems with heterogeneous source code or legacy systems with missing source code. In addition, this approach is based solely on information visible and understandable to domain users; therefore, it can be efficiently used by domain experts without the support of software developers. We evaluate our approach with a case study on a large‐scale enterprise system, in which we demonstrate how up to 65% of the source code dependences and 77% of the database dependences are predicted solely based on domain information. Copyright © 2013 John Wiley & Sons, Ltd.

Knowledge of similar code fragments, also known as code clones, is important to many software maintenance activities including bug fixing, refactoring, impact analysis and program comprehension. While a great deal of research has been conducted for finding techniques and implementing tools to identify code clones, little research has been done to analyze the relationships between code clones and other aspects of software. In this paper, we attempt to uncover the relationships between code clones and coupling among domain-level components. We report on a case study of a large-scale open source enterprise system, where we demonstrate that the probability of finding code clones among components with domain-based coupling is more than 90%. While such a probabilistic view does not replace a clone detection tool per se, it certainly has the potential to complement the existing tools by providing the probability of having code clones between software components. For example, it can both reduce the clone search space and provide a flexible and language independent way of focusing only on a specific part of the system. It can also provide a higher level of abstraction to look at the cloning relationships among software components.

Identity awareness of research data has been introduced as a requirement for open research, transparency and reusability of research data in the context of eScience. This requirement includes the capability of linking research data to researchers, research projects and publications, and identifying the license for the data. This connectivity between research data and other elements in research ecosystems is required in order to make the data available and reusable beyond the initial research. In this paper, we examine these capabilities in the domains of veillance and social computing. The dataset cases presented in this paper articulate the challenges that researchers face as they seek to expose data created as a result of research activities.

Knowledge of software dependencies plays an important role in program comprehension and other maintenance activities. Traditionally, dependencies are derived by source code analysis, however, such an approach can be difficult to use in multi-tier hybrid software systems, or legacy applications where conventional code analysis tools simply do not work as is. In this paper, we propose a hybrid approach to detecting software dependencies by combining conceptual and domain-based coupling metrics. In recent years, a great deal of research focused on deriving various coupling metrics from these sources of information with the aim of assisting software maintainers. Conceptual metrics specify underlying relationships encoded by developers in identifiers and comments of source code classes whereas domain metrics exploit coupling manifested in domain-level information of software components and it is independent from software implementation. The proposed approach is independent from programming language, as such it can be used in multi-tier hybrid systems or legacy applications. We report the results of an empirical case study on a large-scale enterprise system where we demonstrate that the combined approach is able to detect database and source code dependencies with higher precision and recall as compared to its standalone constituents.

Change propagation has mainly been estimated by maintenance history or source code analysis. However, sometimes history and code are inaccessible, or impractical to analyse, such as for heterogeneous sources. Previously we hypothesised that change propagation from modifying domain level components may be predicted purely from information available to domain users. We proposed domain-based change propagation analysis, enabling analysts and domain experts to predict conceptual coupling independent of implementation. This paper reports on application of domain-based analysis to a significant (enterprise) system. We performed both domain- based analysis and a well known history-based analysis and compared the results. Like history-based approaches, domain-based analysis reveals coupling between software components, can assist to prevent errors in software maintenance, and predict change propagation. We conclude that it may be worth applying to certain kinds of systems where established approaches would be considered impractical.