Do information retrieval algorithms for automated traceability perform effectively on issue tracking system data?
Traces between issues in issue tracking systems connect bug reports to software features, they connect competing implementation ideas for a software feature or they identify duplicate issues. However, the trace quality is usually very low. To improve the trace quality between requirements, features,...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Kapitel/Artikel Konferenzschrift |
| Sprache: | Englisch |
| Veröffentlicht: |
04 March 2016
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| In: |
Requirements Engineering: Foundation for Software Quality
Year: 2016, Pages: 45-62 |
| DOI: | 10.1007/978-3-319-30282-9_4 |
| Online-Zugang: | Resolving-System, Volltext: http://dx.doi.org/10.1007/978-3-319-30282-9_4 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-319-30282-9_4 
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| Verfasserangaben: | Thorsten Merten, Daniel Krämer, Bastian Mager, Paul Schell, Simone Bürsner, and Barbara Paech |
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| 520 | |a Traces between issues in issue tracking systems connect bug reports to software features, they connect competing implementation ideas for a software feature or they identify duplicate issues. However, the trace quality is usually very low. To improve the trace quality between requirements, features, and bugs, information retrieval algorithms for automated trace retrieval can be employed. Prevailing research focusses on structured and well-formed documents, such as natural language requirement descriptions. In contrast, the information in issue tracking systems is often poorly structured and contains digressing discussions or noise, such as code snippets, stack traces, and links. | ||
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