A survey on machine learning techniques for source code analysis
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
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 …
The impact of feature importance methods on the interpretation of defect classifiers
Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely
used (often interchangeably) by prior studies to derive feature importance ranks from a …
used (often interchangeably) by prior studies to derive feature importance ranks from a …
Software defect prediction analysis using machine learning techniques
There is always a desire for defect-free software in order to maintain software quality for
customer satisfaction and to save testing expenses. As a result, we examined various known …
customer satisfaction and to save testing expenses. As a result, we examined various known …
Practitioners' perceptions of the goals and visual explanations of defect prediction models
J Jiarpakdee, CK Tantithamthavorn… - 2021 IEEE/ACM 18th …, 2021 - ieeexplore.ieee.org
Software defect prediction models are classifiers that are constructed from historical software
data. Such software defect prediction models have been proposed to help developers …
data. Such software defect prediction models have been proposed to help developers …
A systematic literature review on explainability for machine/deep learning-based software engineering research
The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in
Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment …
Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment …
Innovative approach for predicting biogas production from large-scale anaerobic digester using long-short term memory (LSTM) coupled with genetic algorithm (GA)
An artificial neural network (ANN) model called long-short term memory (LSTM), coupled
with a genetic algorithm (GA) for feature selection, was used to predict biogas production of …
with a genetic algorithm (GA) for feature selection, was used to predict biogas production of …
[PDF][PDF] Cross project software defect prediction using machine learning: a review
MS Saeed, M Saleem - International Journal of Computational …, 2023 - researchgate.net
Software defect prediction is a crucial area of study focused on enhancing software quality
and cutting down on software upkeep expenses. Cross Project Defect Prediction (CPDP) is …
and cutting down on software upkeep expenses. Cross Project Defect Prediction (CPDP) is …
Dealing with imbalanced data for interpretable defect prediction
Y Gao, Y Zhu, Y Zhao - Information and software technology, 2022 - Elsevier
Context Interpretation has been considered as a key factor to apply defect prediction in
practice. As interpretation from rule-based interpretable models can provide insights about …
practice. As interpretation from rule-based interpretable models can provide insights about …
[HTML][HTML] A survey on machine learning techniques applied to source code
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …