Jitline: A simpler, better, faster, finer-grained just-in-time defect prediction
C Pornprasit… - 2021 IEEE/ACM 18th …, 2021 - ieeexplore.ieee.org
A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-
introducing. Recently, CC2Vec-a deep learning approach for Just-In-Time defect prediction …
introducing. Recently, CC2Vec-a deep learning approach for Just-In-Time defect prediction …
Search based training data selection for cross project defect prediction
Context: Previous studies have shown that steered training data or dataset selection can
lead to better performance for cross project defect prediction (CPDP). On the other hand …
lead to better performance for cross project defect prediction (CPDP). On the other hand …
Cross-version defect prediction via hybrid active learning with kernel principal component analysis
As defects in software modules may cause product failure and financial loss, it is critical to
utilize defect prediction methods to effectively identify the potentially defective modules for a …
utilize defect prediction methods to effectively identify the potentially defective modules for a …
A machine learning approach to improve the detection of ci skip commits
R Abdalkareem, S Mujahid… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Continuous integration (CI) frameworks, such as Travis CI, are growing in popularity,
encouraged by market trends towards speeding up the release cycle and building higher …
encouraged by market trends towards speeding up the release cycle and building higher …
Deep just-in-time defect prediction: how far are we?
Defect prediction aims to automatically identify potential defective code with minimal human
intervention and has been widely studied in the literature. Just-in-Time (JIT) defect prediction …
intervention and has been widely studied in the literature. Just-in-Time (JIT) defect prediction …
An investigation of cross-project learning in online just-in-time software defect prediction
Just-In-Time Software Defect Prediction (JIT-SDP) is concerned with predicting whether
software changes are defect-inducing or clean based on machine learning classifiers …
software changes are defect-inducing or clean based on machine learning classifiers …
Understanding the automated parameter optimization on transfer learning for cross-project defect prediction: an empirical study
Data-driven defect prediction has become increasingly important in software engineering
process. Since it is not uncommon that data from a software project is insufficient for training …
process. Since it is not uncommon that data from a software project is insufficient for training …
An empirical examination of the impact of bias on just-in-time defect prediction
Background: Just-In-Time (JIT) defect prediction models predict if a commit will introduce
defects in the future. DeepJIT and CC2Vec are two state-of-the-art JIT Deep Learning (DL) …
defects in the future. DeepJIT and CC2Vec are two state-of-the-art JIT Deep Learning (DL) …
Software defect prediction based on gated hierarchical LSTMs
H Wang, W Zhuang, X Zhang - IEEE Transactions on Reliability, 2021 - ieeexplore.ieee.org
Software defect prediction, aimed at assisting software practitioners in allocating test
resources more efficiently, predicts the potential defective modules in software products …
resources more efficiently, predicts the potential defective modules in software products …
SLDeep: Statement-level software defect prediction using deep-learning model on static code features
Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus
testing activities on more suspicious portions. Consequently, high-quality software is …
testing activities on more suspicious portions. Consequently, high-quality software is …