Tooling for Developing Data-Driven Applications: Overview and Outlook
| dc.contributor.author | Weber, Thomas | |
| dc.contributor.author | Hußmann, Heinrich | |
| dc.contributor.editor | Mühlhäuser, Max | |
| dc.contributor.editor | Reuter, Christian | |
| dc.contributor.editor | Pfleging, Bastian | |
| dc.contributor.editor | Kosch, Thomas | |
| dc.contributor.editor | Matviienko, Andrii | |
| dc.contributor.editor | Gerling, Kathrin|Mayer, Sven | |
| dc.contributor.editor | Heuten, Wilko | |
| dc.contributor.editor | Döring, Tanja | |
| dc.contributor.editor | Müller, Florian | |
| dc.contributor.editor | Schmitz, Martin | |
| dc.date.accessioned | 2022-08-31T09:43:04Z | |
| dc.date.available | 2022-08-31T09:43:04Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Machine Learning systems are, by now, an essential part of the software landscape. From the development perspective this means a paradigmatic shift, which should be reflected in the way we write software. For now, the majority of developers relies on traditional tools for data-driven development, though. To determine how research into tools is catching up, we conducted a systematic literature review, searching for tools dedicated to data-driven development. Of the 1511 search results, we analyzed 76 relevant publications in detail. The diverse sample indicated a strong interest in this topic from different domains, with different approaches and methods. While there are a number of common trends, e.g. the use of visualization, in these tools, only a limited, although increasing, number of these tools has so far been evaluated comprehensively. We therefore summarize trends, strengths and weaknesses in the status quo for data-driven development tools and conclude with a number of potential future directions this field. | en |
| dc.description.uri | https://dl.acm.org/doi/10.1145/3543758.3543779 | en |
| dc.identifier.doi | 10.1145/3543758.3543779 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39260 | |
| dc.language.iso | en | |
| dc.publisher | ACM | |
| dc.relation.ispartof | Mensch und Computer 2022 - Tagungsband | |
| dc.relation.ispartofseries | Mensch und Computer | |
| dc.subject | Literature Review | |
| dc.subject | Software Development | |
| dc.subject | Tools | |
| dc.subject | Machine Learning | |
| dc.subject | Data-Driven Development | |
| dc.title | Tooling for Developing Data-Driven Applications: Overview and Outlook | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 77 | |
| gi.citation.publisherPlace | New York | |
| gi.citation.startPage | 66 | |
| gi.conference.date | 4.-7. September 2022 | |
| gi.conference.location | Darmstadt | |
| gi.conference.sessiontitle | MCI-SE02: Tools and Technology | |
| gi.document.quality | digidoc |