Comparing Multivariate Time Series Analysis and Machine Learning Performance for Technical Debt Prediction : The SQALE Index Case
Robredo, Mikel; Saarimäki, Nyyti; Peñaloza, Rafael; Taibi, Davide; Lenarduzzi, Valentina (2024)
Robredo, Mikel
Saarimäki, Nyyti
Peñaloza, Rafael
Taibi, Davide
Lenarduzzi, Valentina
ACM
2024
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202407307800
https://urn.fi/URN:NBN:fi:tuni-202407307800
Kuvaus
Peer reviewed
Tiivistelmä
Predicting Technical Debt has become a popular research niche in recent software engineering literature. However, there is no consistent approach yet that succeeds in entirely capturing the nature of this type of data. We applied each technique on a dataset consisting of the commit data of a total of 28 Java projects. We predicted the future values of the SQALE index to evaluate their predictive performance. Using these techniques we confirmed the predictive power of each of them with the same commit data. We aim to investigate further the time-dependent nature of other types of commit data to validate the existing prediction techniques.
Kokoelmat
- TUNICRIS-julkaisut [19011]