<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>EconStor Collection:</title>
    <link>https://hdl.handle.net/10419/58410</link>
    <description />
    <pubDate>Wed, 29 Apr 2026 12:28:42 GMT</pubDate>
    <dc:date>2026-04-29T12:28:42Z</dc:date>
    <item>
      <title>An efficient implementation of a runtime for constraint-logic object-oriented programming</title>
      <link>https://hdl.handle.net/10419/283891</link>
      <description>Title: An efficient implementation of a runtime for constraint-logic object-oriented programming
Authors: Winkelmann, Hendrik
Abstract: In this technical report the symbolic execution engine 'Mulib' will be outlined in its architecture, main algorithms, and design decisions. Mulib employs a rather new approach of program transformation (see Section 2) and thus, technical details on the implementation of such a system are thought to be insightful. Mulib first and foremost implements the runtime semantics of the Constraint-Logic Object-Oriented Programming (CLOOP) language Muli. Muli employs symbolic execution as a technique to, e.g., solve constraint-satisfaction problems and generate test cases.</description>
      <pubDate>Mon, 01 Jan 2024 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/283891</guid>
      <dc:date>2024-01-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>A testing tool visualizing and ensuring data-flow coverage</title>
      <link>https://hdl.handle.net/10419/287758</link>
      <description>Title: A testing tool visualizing and ensuring data-flow coverage
Authors: Troost, Laura
Abstract: According to different studies, data-flow coverage is more effective in exposing errors than common control-flow metrics. However, there are not many suitable and available data-flow analysis tools. This report illustrates the technical details of the implementation of Dacite (Data-flow Coverage for Imperative Testing). Dacite is an open-source tool able to dynamically derive the covered data flow of a given Java program and its JUnit test cases. Using the Language Server Protocol, it visualizes the data flow within common IDEs. Moreover, Dacite provides feedback about the not-covered data flow and automatically generates test cases for these by using symbolic execution.</description>
      <pubDate>Mon, 01 Jan 2024 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/287758</guid>
      <dc:date>2024-01-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>Inversions: The good and the bad of digitalization</title>
      <link>https://hdl.handle.net/10419/323201</link>
      <description>Title: Inversions: The good and the bad of digitalization
Editors: Klein, Stefan; Totz, Carsten
Abstract: This working paper combines four student contributions whose themes highlight the pertinence of studying the inversion of digital technologies' outcomes: - Disinformation as part of Russias Information Warfare - The impact of algorithmic management on labour relations - Implications of Chat GPT for students in higher education - The contribution of blended teaching on the sustainability of institutions of higher education. As such it encourages the reader to not only be attentive to the potential abuses of technologies that are meant to create value(s) for society but also to examine the mechanisms of inversion: how and when do positive impacts turn into negative ones? Doing so is a first steps to "own one's future": to overcome technology cynicism and explore ways of making our world a better place.</description>
      <pubDate>Mon, 01 Jan 2024 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/323201</guid>
      <dc:date>2024-01-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests</title>
      <link>https://hdl.handle.net/10419/234061</link>
      <description>Title: Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests
Authors: Lechtenberg, Sandra; Hellingrath, Bernd
Abstract: Advances in the area of computing power, data storage capabilities, etc., are changing the way business is done, particularly regarding how businesses use and apply artificial intelligence. To better understand how artificial intelligence is used in supply chain management, this paper identifies and compares the main research fields investigating this topic as well as the primary industry interests in it. For this, we performed a structured literature review that shows which methods of artificial intelligence are applied to which problems of supply chain management in the scientific literature. Then, we present industry-driven applications to provide an overview of fields that are most relevant to industry. Based on these results, indications for future research are derived.</description>
      <pubDate>Fri, 01 Jan 2021 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/234061</guid>
      <dc:date>2021-01-01T00:00:00Z</dc:date>
    </item>
  </channel>
</rss>

