Industrial adoption of machine learning techniques for early identification of invalid bug reports
In: Empirical Software Engineering ELLIIT: the Linköping-Lund initiative on IT and mobile communication, 2024
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Zugriff:
Despite the accuracy of machine learning (ML) techniques in predict-ing invalid bug reports, as shown in earlier research, and the importance of earlyidentification of invalid bug reports in software maintenance, the adoption of MLtechniques for this task in industrial practice is yet to be investigated. In this study,we used a technology transfer model to guide the adoption of an ML technique at acompany for the early identification of invalid bug reports. In the process, we alsoidentify necessary conditions for adopting such techniques in practice. We followeda case study research approach with various design and analysis iterations for tech-nology transfer activities. We collected data from bug repositories, through focusgroups, a questionnaire, and a presentation and feedback session with an expert.As expected, we found that an ML technique can identify invalid bug reports withacceptable accuracy at an early stage. However, the technique’s accuracy dropsover time in its operational use due to changes in the product, the used technolo-gies, or the development organization. Such changes may require retraining theML model. During validation, practitioners highlighted the need to understandthe ML technique’s predictions to trust the predictions. We found that a visual(using a state-of-the-art ML interpretation framework) and descriptive explana-tion of the prediction increases the trustability of the technique compared to justpresenting the results of the validity predictions. We conclude that trustability,integration with the existing toolchain, and maintaining the techniques’ accuracyover time are critical for increasing the likelihood of adoption.
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Industrial adoption of machine learning techniques for early identification of invalid bug reports
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Autor/in / Beteiligte Person: | Laiq, Muhammad ; bin Ali, Nauman ; Börstler, Jürgen ; Engström, Emelie |
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Zeitschrift: | Empirical Software Engineering ELLIIT: the Linköping-Lund initiative on IT and mobile communication, 2024 |
Veröffentlichung: | 2024 |
Medientyp: | unknown |
ISSN: | 1573-7616 (print) |
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