A systematic review of machine learning techniques for software fault prediction
R Malhotra - Applied Soft Computing, 2015 - Elsevier
Background Software fault prediction is the process of developing models that can be used
by the software practitioners in the early phases of software development life cycle for …
by the software practitioners in the early phases of software development life cycle for …
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 …
The future of employment: How susceptible are jobs to computerisation?
CB Frey, MA Osborne - Technological forecasting and social change, 2017 - Elsevier
We examine how susceptible jobs are to computerisation. To assess this, we begin by
implementing a novel methodology to estimate the probability of computerisation for 702 …
implementing a novel methodology to estimate the probability of computerisation for 702 …
Deep semantic feature learning for software defect prediction
Software defect prediction, which predicts defective code regions, can assist developers in
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …
Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans
We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D
scan of a scene as input and predicting a complete 3D model along with per-voxel semantic …
scan of a scene as input and predicting a complete 3D model along with per-voxel semantic …
Cc2vec: Distributed representations of code changes
Existing work on software patches often use features specific to a single task. These works
often rely on manually identified features, and human effort is required to identify these …
often rely on manually identified features, and human effort is required to identify these …
Heterogeneous defect prediction
Software defect prediction is one of the most active research areas in software engineering.
We can build a prediction model with defect data collected from a software project and …
We can build a prediction model with defect data collected from a software project and …
Deepjit: an end-to-end deep learning framework for just-in-time defect prediction
Software quality assurance efforts often focus on identifying defective code. To find likely
defective code early, change-level defect prediction-aka. Just-In-Time (JIT) defect prediction …
defective code early, change-level defect prediction-aka. Just-In-Time (JIT) defect prediction …
Machine learning based methods for software fault prediction: A survey
SK Pandey, RB Mishra, AK Tripathi - Expert Systems with Applications, 2021 - Elsevier
Several prediction approaches are contained in the arena of software engineering such as
prediction of effort, security, quality, fault, cost, and re-usability. All these prediction …
prediction of effort, security, quality, fault, cost, and re-usability. All these prediction …
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 …