Towards the Evaluation of Relevant Interaction Styles for Software Developers

Privacy by Design (PbD) is the most common approach followed by software developers who aim to reduce risks within their application designs, yet it remains commonplace for developers to retain little conceptual understanding of what is meant by privacy. A vision is to develop an intelligent privacy assistant to whom developers can easily ask questions to learn how to incorporate different privacy-preserving ideas into their IoT application designs. This article lays the foundations toward developing such a privacy assistant by synthesising existing PbD knowledge to elicit requirements. It is believed that such a privacy assistant should not just prescribe a list of privacy-preserving ideas that developers should incorporate into their design. Instead, it should explain how each prescribed idea helps to protect privacy in a given application design context—this approach is defined as “Explainable Privacy.” A total of 74 privacy patterns were analysed and reviewed using ten different PbD schemes to understand how each privacy pattern is built and how each helps to ensure privacy. Due to page limitations, we have presented a detailed analysis in Reference [3]. In addition, different real-world Internet of Things (IoT) use-cases, including a healthcare application, were used to demonstrate how each privacy pattern could be applied to a given application design. By doing so, several knowledge engineering requirements were identified that need to be considered when developing a privacy assistant. It was also found that, when compared to other IoT application domains, privacy patterns can significantly benefit healthcare applications. In conclusion, this article identifies the research challenges that must be addressed if one wishes to construct an intelligent privacy assistant that can truly augment software developers’ capabilities at the design phase.

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Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge

Nature Methods ◽

10.1038/s41592-020-01051-w ◽

2021 ◽

Vol 18 (2) ◽

pp. 156-164 ◽

Cited By ~ 4

Author(s):

Catherine L. Lawson ◽

Andriy Kryshtafovych ◽

Paul D. Adams ◽

Pavel V. Afonine ◽

Matthew L. Baker ◽

...

Keyword(s):

Electron Microscopy ◽

Structure Data ◽

Model Evaluation ◽

Data Bank ◽

High Accuracy ◽

Atomic Resolution ◽

Software Developers ◽

Data Archives ◽

Modeling Software

AbstractThis paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.

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Women in the Singlet Fission World: Pearls in a Semi-Open Shell

Molecules ◽

10.3390/molecules26102922 ◽

2021 ◽

Vol 26 (10) ◽

pp. 2922

Author(s):

Joanna Stoycheva ◽

Julia Romanova ◽

Alia Tadjer

Keyword(s):

Solar Cell ◽

Focal Point ◽

Open Shell ◽

Generation Process ◽

Singlet Fission ◽

Software Developers ◽

Multiple Exciton Generation ◽

Cell Technologies ◽

The Subject

Singlet fission, a multiple exciton generation process, can revolutionize existing solar cell technologies. Offering the possibility to double photocurrent, the process has become a focal point for physicists, chemists, software developers, and engineers. The following review is dedicated to the female investigators, predominantly theorists, who have contributed to the field of singlet fission. We highlight their most significant advances in the subject, from deciphering the mechanism of the process to designing coveted singlet fission materials.

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Sifu - a cybersecurity awareness platform with challenge assessment and intelligent coach

Cybersecurity ◽

10.1186/s42400-020-00064-4 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Tiago Espinha Gasiba ◽

Ulrike Lechner ◽

Maria Pinto-Albuquerque

Keyword(s):

Real Life ◽

Critical Infrastructures ◽

Automatic Assessment ◽

Software Developers ◽

Software Vulnerabilities ◽

Secure Coding ◽

Industrial Software ◽

Industrial Standards ◽

Coding Guidelines ◽

Industrial Context

AbstractSoftware vulnerabilities, when actively exploited by malicious parties, can lead to catastrophic consequences. Proper handling of software vulnerabilities is essential in the industrial context, particularly when the software is deployed in critical infrastructures. Therefore, several industrial standards mandate secure coding guidelines and industrial software developers’ training, as software quality is a significant contributor to secure software. CyberSecurity Challenges (CSC) form a method that combines serious game techniques with cybersecurity and secure coding guidelines to raise secure coding awareness of software developers in the industry. These cybersecurity awareness events have been used with success in industrial environments. However, until now, these coached events took place on-site. In the present work, we briefly introduce cybersecurity challenges and propose a novel platform that allows these events to take place online. The introduced cybersecurity awareness platform, which the authors call Sifu, performs automatic assessment of challenges in compliance to secure coding guidelines, and uses an artificial intelligence method to provide players with solution-guiding hints. Furthermore, due to its characteristics, the Sifu platform allows for remote (online) learning, in times of social distancing. The CyberSecurity Challenges events based on the Sifu platform were evaluated during four online real-life CSC events. We report on three surveys showing that the Sifu platform’s CSC events are adequate to raise industry software developers awareness on secure coding.

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Individual differences limit predicting well-being and productivity using software repositories: a longitudinal industrial study

Empirical Software Engineering ◽

10.1007/s10664-021-09977-1 ◽

2021 ◽

Vol 26 (5) ◽

Author(s):

Miikka Kuutila ◽

Mika Mäntylä ◽

Maëlick Claes ◽

Marko Elovainio ◽

Bram Adams

Keyword(s):

Individual Differences ◽

Fixed Effects ◽

Prediction Models ◽

Well Being ◽

Software Project ◽

Structured Interviews ◽

Software Developers ◽

Software Repositories ◽

Generalized Linear Mixed Effects ◽

Individualized Prediction

AbstractReports of poor work well-being and fluctuating productivity in software engineering have been reported in both academic and popular sources. Understanding and predicting these issues through repository analysis might help manage software developers’ well-being. Our objective is to link data from software repositories, that is commit activity, communication, expressed sentiments, and job events, with measures of well-being obtained with a daily experience sampling questionnaire. To achieve our objective, we studied a single software project team for eight months in the software industry. Additionally, we performed semi-structured interviews to explain our results. The acquired quantitative data are analyzed with generalized linear mixed-effects models with autocorrelation structure. We find that individual variance accounts for most of the R2 values in models predicting developers’ experienced well-being and productivity. In other words, using software repository variables to predict developers’ well-being or productivity is challenging due to individual differences. Prediction models developed for each developer individually work better, with fixed effects R2 value of up to 0.24. The semi-structured interviews give insights into the well-being of software developers and the benefits of chat interaction. Our study suggests that individualized prediction models are needed for well-being and productivity prediction in software development.

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