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On the feasibility of automated prediction of bug and non-bug issues

Herbold, Steffen; Trautsch, A.; Trautsch, F.

Abstract:
Context
Issue tracking systems are used to track and describe tasks in the development process, e.g., requested feature improvements or reported bugs. However, past research has shown that the reported issue types often do not match the description of the issue.

Objective
We want to understand the overall maturity of the state of the art of issue type prediction with the goal to predict if issues are bugs and evaluate if we can improve existing models by incorporating manually specified knowledge about issues.

Method
We train different models for the title and description of the issue to account for the difference in structure between these fields, e.g., the length. Moreover, we manually detect issues whose description contains a null pointer exception, as these are strong indicators that issues are bugs.

Results
Our approach performs best overall, but not significantly different from an approach from the literature based on the fastText classifier from Facebook AI Research. The small improvements in prediction performance are due to structural information about the issues we used. We found that using information about the content of issues in form of null pointer exceptions is not useful. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000125241
Veröffentlicht am 23.10.2020
Originalveröffentlichung
DOI: 10.1007/s10664-020-09885-w
Web of Science
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1382-3256, 1573-7616
KITopen-ID: 1000125241
Erschienen in Empirical software engineering
Schlagwörter Issue type prediction; Mislabeled issues; Issue tracking
Nachgewiesen in Web of Science
Scopus
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