A literature review of using machine learning in software development life cycle stages
The software engineering community is rapidly adopting machine learning for transitioning
modern-day software towards highly intelligent and self-learning systems. However, the …
modern-day software towards highly intelligent and self-learning systems. However, the …
[HTML][HTML] On the use of deep learning in software defect prediction
Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction
Context: In practice, software datasets tend to have more non-defective instances than
defective ones, which is referred to as the class imbalance problem in software defect …
defective ones, which is referred to as the class imbalance problem in software defect …
Research progress of software defect prediction
宫丽娜， 姜淑娟， 姜丽 - Journal of Software, 2019 - jos.org.cn
随着软件规模的扩大和复杂度的不断提高, 软件的质量问题成为关注的焦点,
软件缺陷是软件质量的对立面, 威胁着软件质量, 如何在软件开发的早期挖掘出缺陷模块成为 …
软件缺陷是软件质量的对立面, 威胁着软件质量, 如何在软件开发的早期挖掘出缺陷模块成为 …
The best of both worlds: integrating semantic features with expert features for defect prediction and localization
To improve software quality, just-in-time defect prediction (JIT-DP)(identifying defect-
inducing commits) and just-in-time defect localization (JIT-DL)(identifying defect-inducing …
inducing commits) and just-in-time defect localization (JIT-DL)(identifying defect-inducing …
An empirical study on pareto based multi-objective feature selection for software defect prediction
The performance of software defect prediction (SDP) models depend on the quality of
considered software features. Redundant features and irrelevant features may reduce the …
considered software features. Redundant features and irrelevant features may reduce the …
Revisiting supervised and unsupervised methods for effort-aware cross-project defect prediction
Cross-project defect prediction (CPDP), aiming to apply defect prediction models built on
source projects to a target project, has been an active research topic. A variety of supervised …
source projects to a target project, has been an active research topic. A variety of supervised …
Cross project defect prediction via balanced distribution adaptation based transfer learning
Defect prediction assists the rational allocation of testing resources by detecting the
potentially defective software modules before releasing products. When a project has no …
potentially defective software modules before releasing products. When a project has no …
Software defect number prediction: Unsupervised vs supervised methods
Context: Software defect number prediction (SDNP) can rank the program modules
according to the prediction results and is helpful for the optimization of testing resource …
according to the prediction results and is helpful for the optimization of testing resource …
How far does the predictive decision impact the software project? The cost, service time, and failure analysis from a cross-project defect prediction model
Context: Cross-project defect prediction (CPDP) models are being developed to optimise the
testing resources. Objectives: Proposing an ensemble classification framework for CPDP as …
testing resources. Objectives: Proposing an ensemble classification framework for CPDP as …