- Tytuł:
- Examining the Predictive Capability of Advanced Software Fault Prediction Models – An Experimental Investigation Using Combination Metrics
- Autorzy:
-
Sharma, Pooja
Sangal, Amrit Lal - Powiązania:
- https://bibliotekanauki.pl/articles/2060915.pdf
- Data publikacji:
- 2022
- Wydawca:
- Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
- Tematy:
-
process product
process metrics
classifiers
ensemble design
software
fault prediction
software quality - Opis:
- Background: Fault prediction is a key problem in software engineering domain. In recent years, an increasing interest in exploiting machine learning techniques to make informed decisions to improve software quality based on available data has been observed. Aim: The study aims to build and examine the predictive capability of advanced fault prediction models based on product and process metrics by using machine learning classifiers and ensemble design. Method: Authors developed a methodological framework, consisting of three phases i.e., (i) metrics identification (ii) experimentation using base ML classifiers and ensemble design (iii) evaluating performance and cost sensitiveness. The study has been conducted on 32 projects from the PROMISE, BUG, and JIRA repositories. Result: The results shows that advanced fault prediction models built using ensemble methods show an overall median of $F$-score ranging between 76.50% and 87.34% and the ROC(AUC) between 77.09% and 84.05% with better predictive capability and cost sensitiveness. Also, non-parametric tests have been applied to test the statistical significance of the classifiers. Conclusion: The proposed advanced models have performed impressively well for inter project fault prediction for projects from PROMISE, BUG, and JIRA repositories.
- Źródło:
-
e-Informatica Software Engineering Journal; 2022, 16, 1; art. no. 220104
1897-7979 - Pojawia się w:
- e-Informatica Software Engineering Journal
- Dostawca treści:
- Biblioteka Nauki
Artykuł