Predicting Prime Path Coverage Using Regression Analysis
| Autor: | Keslley Silva, Erika Cota |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021103 operations research
Mean squared error Computer science 0211 other engineering and technologies Code coverage Word error rate 020207 software engineering Regression analysis 02 engineering and technology computer.software_genre Task (project management) Knowledge extraction Regression testing 0202 electrical engineering electronic engineering information engineering Test suite Data mining computer |
| Zdroj: | SBES |
| DOI: | 10.1145/3422392.3422413 |
| Popis: | Test coverage criteria help the tester to analyze the quality of the test suite, especially in an evolving system where it can be used to guide the prioritization of regression tests as well as the testing effort of new code. However, coverage analysis of more powerful criteria such as path coverage is still a challenging task due to the lack of supporting tools. In this paper, we evaluate the opportunity of using machine learning algorithms to estimate the prime-path coverage of a test suite at method level. We use a knowledge discovery in database framework and a dataset built from real-world projects to devise a regression model for prime-path prediction. Experimental results demonstrate that a suitable predictive model can be defined with RMSE 0.118 on cross-validation and an average error rate of 26% in a cross-project scenario. We also show how the tester can use the proposed strategy in a practical cross-project scenario. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|