Improving Science That Uses Code

As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being properly supported by competent statistics.If and when code avoids adequate scrutiny, science becomes unreliable and unverifiable because results — text, data, graphs, images, etc — depend on untrustworthy code.Currently, scientists rarely assure the quality of the code they rely on, and rarely make it accessible for scrutiny. Even when available, scientists rarely provide adequate documentation to understand or use it reliably.This paper proposes and justifies ways to improve science using code:1. Professional Software Engineers can help, particularly in critical fields such as public health, climate change and energy.2. ‘Software Engineering Boards,’ analogous to Ethics or Institutional Review Boards, should be instigated and used.3. The Reproducible Analytic Pipeline (RAP) methodology can be generalized to cover code and Software Engineering methodologies, in a generalization this paper introduces called RAP+. RAP+ (or comparable interventions) could be supported and or even required in journal, conference and funding body policies.The paper’s Supplemental Material provides a summary of Software Engineering best practice relevant to scientific research, including further suggestions for RAP+ workflows.‘Science is what we understand well enough to explain to a computer.’ Donald E. Knuth in A=B [ 1]‘I have to write to discover what I am doing.’ Flannery O’Connor, quoted in Write for your life [ 2]‘Criticism is the mother of methodology.’ Robert P. Abelson in Statistics as Principled Argument [ 3]‘From its earliest times, science has operated by being open and transparent about methods and evidence, regardless of which technology has been in vogue.’ Editorial in Nature [4

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Global perspectives on legacy systems

Summarises findings of two international workshops on legacy systems, held in conjunction with an EPSRC managed programme. Issues covered include the nature and dynamics of legacy systems, the co-evolution of software and organisations, issues around software as a technology (its engineering and its management), and organisational/people issues

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Rapid Manufactured Textiles

Rapid Manufacturing (RM) is increasingly becoming a viable manufacturing process due to dramatic advantages that are achievable in the area of design complexity. Through the exploration of the design freedom, this paper introduces the concept of manufacturing textiles for potential smart and high performance textile applications. This paper discusses the current limitations associated with the manufacture of textiles through RM and presents a novel methodology for the generation of 3D conformal RM textile articles. The paper concludes that through RM it is entirely possible to manufacture a structure that incorporates drape and free movement properties directly comparable to conventional textiles.Mechanical Engineerin

On Evidence-based Risk Management in Requirements Engineering

Background: The sensitivity of Requirements Engineering (RE) to the context makes it difficult to efficiently control problems therein, thus, hampering an effective risk management devoted to allow for early corrective or even preventive measures. Problem: There is still little empirical knowledge about context-specific RE phenomena which would be necessary for an effective context- sensitive risk management in RE. Goal: We propose and validate an evidence-based approach to assess risks in RE using cross-company data about problems, causes and effects. Research Method: We use survey data from 228 companies and build a probabilistic network that supports the forecast of context-specific RE phenomena. We implement this approach using spreadsheets to support a light-weight risk assessment. Results: Our results from an initial validation in 6 companies strengthen our confidence that the approach increases the awareness for individual risk factors in RE, and the feedback further allows for disseminating our approach into practice.Comment: 20 pages, submitted to 10th Software Quality Days conference, 201