Elixir (programming language)

Abstract: We present Bugs.jar, a large-scale dataset for research in automated debugging, patching, and testing of Java programs. Bugs.jar is comprised of 1,158 bugs and patches, drawn from 8 large, popular open-source Java projects, spanning 8 diverse and prominent application categories. It is an order of magnitude larger than Defects4J, the only other dataset in its class. We discuss the methodology used for constructing Bugs.jar, the representation of the dataset, several use-cases, and an illustration of three of the use-cases through the application of 3 specific tools on Bugs.jar, namely our own tool, Elixir, and two third-party tools, Ekstazi and JaCoCo.

Abstract: Motivation: Life science research in academia, industry, agriculture, and the health sector depends critically on free and open data resources. ELIXIR (www.elixir-europe.org), the European Research Infrastructure for life sciences data, has identified a set of Core Data Resources within Europe that are of most fundamental importance for the long-term preservation of biological data. We explore characteristics of their usage, impact and assured funding horizon to assess their value and importance as an infrastructure, to understand sustainability of the infrastructure, and to demonstrate a model for assessing Core Data Resources worldwide. Results: The nineteen resources currently designated ELIXIR Core Data Resources form a data infrastructure in Europe which is a subset of the worldwide open life science data infrastructure. We show that, from 2014 to 2018, data managed by the Core Data Resources more than tripled while staff numbers increased by less than a tenth. Additionally, support for the Core Data Resources is precarious: together they have assured funding for less than a third of current staff after four years. Our findings demonstrate the importance of the ELIXIR Core Data Resources as repositories for research data and knowledge, while also demonstrating the uncertain nature of the funding environment for this infrastructure. ELIXIR is working towards longer-term support for the Core Data Resources and, through the Global Biodata Coalition, aims to ensure support for the worldwide life science data resource infrastructure of which the ELIXIR Core Data Resources are a subset.

Abstract: Several languages for querying and transforming XML, including XML-QL, Quilt, and XQL, have been proposed. However, these languages do not support ranked queries based on textual similarity, in the spirit of traditional IR. Several extensions to these XML query languages to support keyword search have been made, but the resulting languages cannot express IR-style queries such as "find books and CDs with similar titles." In some of these languages keywords are used merely as boolean filters without support for true ranked retrieval; others permit similarity calculations only between a data value and a constant, and thus cannot express the above query. WHIRL avoids both problems, but assumes relational data. We propose ELIXIR, an expressive and efficient language for XML information retrieval that extends XML-QL with a textual similarity operator that can be used for similarity joins, so ELIXIR is sufficiently expressive to handle the sample query above. ELIXIR thus qualifies as a general-purpose XML IR query language. Our central contribution is an efficient algorithm for answering ELIXIR queries that rewrites the original ELIXIR query into a series of XML-QL queries to generate intermediate relational data, and uses WHIRL to efficiently evaluate the similarity operators on this intermediate data, yielding an XML document with nodes ranked by similarity. Our experiments demonstrate that our prototype scales well with the size of the query and the XML data.