End-user driven feedback prioritization

End-user feedback is becoming more important for the evolution of software systems. There exist various communication channels for end-users (app stores, social networks) which allow them to express their experiences and requirements regarding a software application. End-users communicate a large amount of feedback via these channels which leads to open issues regarding the use of end-user feedback for software development, maintenance and evolution. This includes investigating how to identify relevant feedback scattered across different feedback channels and how to determine the priority of the feedback issues communicated. In this research preview paper, we discuss ideas for enduser driven feedback prioritization.Peer ReviewedPostprint (published version

A situational approach for the definition and tailoring of a data-driven software evolution method

Successful software evolution heavily depends on the selection of the right features to be included in the next release. Such selection is difficult, and companies often report bad experiences about user acceptance. To overcome this challenge, there is an increasing number of approaches that propose intensive use of data to drive evolution. This trend has motivated the SUPERSEDE method, which proposes the collection and analysis of user feedback and monitoring data as the baseline to elicit and prioritize requirements, which are then used to plan the next release. However, every company may be interested in tailoring this method depending on factors like project size, scope, etc. In order to provide a systematic approach, we propose the use of Situational Method Engineering to describe SUPERSEDE and guide its tailoring to a particular context.Peer ReviewedPostprint (author's final draft

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

CGIAR Excellence in Breeding Platform - Plan of Work and Budget 2020

At the end of 2019, all CGIAR centers had submitted improvement plans based on an EiB template and in close collaboration with EiB staff while – in a parallel process with breeding programs, funders and private sector representatives – a vision for breeding program modernization was developed and presented to CGIAR breeding leadership at the EiB Annual Meeting. This vision represents an evolution of EiB in the context of the Crops to End Hunger Initiative (CtEH) beyond the initial scope of providing tools, services and expert advice, and serves as a guide for Center leadership to drive changes with EiB support. In addition, EiB has taken the role of managing and disbursing funding, made available by Funders via CtEH to modernize breeding and enable CGIAR breeding programs to implement the vision provided by EiB

A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions