Abstract
Over the past decade with the rise of the Mining Software Repositories (MSR)field, the modelling of defects for large and long-lived systems has become one of the most common applications of MSR. The findings and approaches of such studies have attracted the attention of many of our industrial collaborators (and other practitioners worldwide). At the core of many of these studies is the development and use of analytical models for defects. In this paper, we discuss common pitfalls and challenges that we observed as practitioners attempt to develop such models or reason about the findings of such studies. The key goal of this paper is to document such pitfalls and challenges so practitioners can avoid them in future efforts. We also hope that other academics will be mindful of such pitfalls and challenges in their own work and industrial engagements.
Original language | English |
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Title of host publication | Proceedings - 2018 ACM/IEEE 40th International Conference on Software Engineering: Software Engineering in Practice - ICSE-SEIP 2018 |
Subtitle of host publication | 30 May – 1 June 2018 Gothenburg, Sweden |
Editors | Ivica Crnkovic |
Place of Publication | New York NY USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 286-295 |
Number of pages | 10 |
ISBN (Electronic) | 9781450356596 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Event | International Conference on Software Engineering 2018: Software Engineering in Practice Track - Gothenburg, Sweden Duration: 27 May 2018 → 3 Jun 2018 Conference number: 40th https://dl.acm.org/doi/proceedings/10.1145/3183519 (Proceedings) |
Conference
Conference | International Conference on Software Engineering 2018 |
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Abbreviated title | ICSE-SEIP 2018 |
Country/Territory | Sweden |
City | Gothenburg |
Period | 27/05/18 → 3/06/18 |
Other | Track from International Conference on Software Engineering 2018 |
Internet address |
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Keywords
- Defect modelling
- Empirical software engineering
- Experimental design
- Mining software repositories
- Software analytics