A systematic survey of just-in-time software defect prediction
Recent years have experienced sustained focus in research on software defect prediction
that aims to predict the likelihood of software defects. Moreover, with the increased interest …
that aims to predict the likelihood of software defects. Moreover, with the increased interest …
Predicting the precise number of software defects: Are we there yet?
X Yu, J Keung, Y ** from remote sensing data using deep forest predictive model
Remote sensing data prove to be an effective resource for constructing a data-driven
predictive model of mineral prospectivity. Nonetheless, existing deep learning models …
predictive model of mineral prospectivity. Nonetheless, existing deep learning models …
Boosting multi-objective just-in-time software defect prediction by fusing expert metrics and semantic metrics
Just-in-time software defect prediction (JIT-SDP) aims to predict whether a code commit is
defect-inducing or defect-clean immediately after developers submit their code commits. In …
defect-inducing or defect-clean immediately after developers submit their code commits. In …
Software aging prediction for cloud services using a gate recurrent unit neural network model based on time series decomposition
Software aging, which is caused by the accumulation of errors in the system and the
consumption of computing resources, tends to occur in long-running cloud service software …
consumption of computing resources, tends to occur in long-running cloud service software …
Exploiting gated graph neural network for detecting and explaining self-admitted technical debts
Self-admitted technical debt (SATD) refers to a specific type of technical debt that is
introduced intentionally in the software development and maintenance processes. SATD …
introduced intentionally in the software development and maintenance processes. SATD …