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

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review

A variety of genome-wide profiling techniques are available to probe complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher-level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we provide a comparison among various modeling procedures for integrating genome-wide profiling data of gene copy number and transcriptional alterations and highlight common approaches to genomic data integration. A transparent benchmarking procedure is introduced to quantitatively compare the cancer gene prioritization performance of the alternative methods. The benchmarking algorithms and data sets are available at http://intcomp.r-forge.r-project.orgComment: PDF file including supplementary material. 9 pages. Preprin

Disinvestment in healthcare: An overview of HTA agencies and organizations activities at European level

Background: In an era of a growing economic pressure for all health systems, the interest for "disinvestment" in healthcare increased. In this context, evidence based approaches such as Health Technology Assessment (HTA) are needed both to invest and to disinvest in health technologies. In order to investigate the extent of application of HTA in this field, methodological projects/frameworks, case studies, dissemination initiatives on disinvestment released by HTA agencies and organizations located in Europe were searched. Methods: In July 2015, the websites of HTA agencies and organizations belonging to the European network for HTA (EUnetHTA) and the International Network of Agencies for HTA (INAHTA) were accessed and searched through the use of the term "disinvestment". Retrieved deliverables were considered eligible if they reported methodological projects/frameworks, case studies and dissemination initiatives focused on disinvestment in healthcare. Results: 62 HTA agencies/organizations were accessed and eight methodological projects/frameworks, one case study and one dissemination initiative were found starting from 2007. With respect to methodological projects/frameworks, two were delivered in Austria, one in Italy, two in Spain and three in U.K. As for the case study and the dissemination initiative, both came from U.K. The majority of deliverables were aimed at making an overview of existing disinvestment approaches and at identifying challenges in their introduction. Conclusions: Today, in a healthcare context characterized by resource scarcity and increasing service demand, "disinvestment" from low-value services and reinvestment in high-value ones is a key strategy that may be supported by HTA. The lack of evaluation of technologies in use, in particular at the end of their lifecycle, may be due to the scant availability of frameworks and guidelines for identification and assessment of obsolete technologies that was shown by our work. Although several projects were carried out in different countries, most remain constrained to the field of research. Disinvestment is a relatively new concept in HTA that could pose challenges also from a methodological point of view. To tackle these challenges, it is necessary to construct experiences at international level with the aim to develop new methodological approaches to produce and grow evidence on disinvestment policies and practices

Testing for the Solicitation Management System

This project is to test the Solicitation Management System software. The SMS is an online system that facilitates processing of a solicitation at the Office of Technology Transfer and Commercialization (OTTO). It allows potential applicants to submit applications to OTTC for further processing

Empirical Evaluation of Mutation-based Test Prioritization Techniques

We propose a new test case prioritization technique that combines both mutation-based and diversity-based approaches. Our diversity-aware mutation-based technique relies on the notion of mutant distinguishment, which aims to distinguish one mutant's behavior from another, rather than from the original program. We empirically investigate the relative cost and effectiveness of the mutation-based prioritization techniques (i.e., using both the traditional mutant kill and the proposed mutant distinguishment) with 352 real faults and 553,477 developer-written test cases. The empirical evaluation considers both the traditional and the diversity-aware mutation criteria in various settings: single-objective greedy, hybrid, and multi-objective optimization. The results show that there is no single dominant technique across all the studied faults. To this end, \rev{we we show when and the reason why each one of the mutation-based prioritization criteria performs poorly, using a graphical model called Mutant Distinguishment Graph (MDG) that demonstrates the distribution of the fault detecting test cases with respect to mutant kills and distinguishment