Automated parameter optimization of classification techniques for defect prediction models
Defect prediction models are classifiers that are trained to identify defect-prone software
modules. Such classifiers have configurable parameters that control their characteristics (eg …
modules. Such classifiers have configurable parameters that control their characteristics (eg …
An effective approach for software project effort and duration estimation with machine learning algorithms
P Pospieszny, B Czarnacka-Chrobot… - Journal of Systems and …, 2018 - Elsevier
During the last two decades, there has been substantial research performed in the field of
software estimation using machine learning algorithms that aimed to tackle deficiencies of …
software estimation using machine learning algorithms that aimed to tackle deficiencies of …
Data quality: Some comments on the nasa software defect datasets
M Shepperd, Q Song, Z Sun… - IEEE Transactions on …, 2013 - ieeexplore.ieee.org
Background--Self-evidently empirical analyses rely upon the quality of their data. Likewise,
replications rely upon accurate reporting and using the same rather than similar versions of …
replications rely upon accurate reporting and using the same rather than similar versions of …
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 …
Interpretability application of the Just-in-Time software defect prediction model
Software defect prediction is one of the most active fields in software engineering. Recently,
some experts have proposed the Just-in-time Defect Prediction Technology. Just-in-time …
some experts have proposed the Just-in-time Defect Prediction Technology. Just-in-time …
An empirical analysis of data preprocessing for machine learning-based software cost estimation
Context Due to the complex nature of software development process, traditional parametric
models and statistical methods often appear to be inadequate to model the increasingly …
models and statistical methods often appear to be inadequate to model the increasingly …
[PDF][PDF] Predictive analytics approaches for software effort estimation: A review
AG Priya Varshini… - Indian J. Sci …, 2020 - pdfs.semanticscholar.org
Abstract Background/Objective: In Software Effort Estimation (SEE), predicting the amount of
time taken in human hours or months for software development is considered as a …
time taken in human hours or months for software development is considered as a …
Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques
In software engineering, the planning and budgeting stages of a software project are of great
importance to all stakeholders, including project managers as well as clients. The estimated …
importance to all stakeholders, including project managers as well as clients. The estimated …
The impact of correlated metrics on the interpretation of defect models
J Jiarpakdee, C Tantithamthavorn… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Defect models are analytical models for building empirical theories related to software
quality. Prior studies often derive knowledge from such models using interpretation …
quality. Prior studies often derive knowledge from such models using interpretation …
A new approach to the QS university ranking using the composite I‐distance indicator: Uncertainty and sensitivity analyses
Some major concerns of universities are to provide quality in higher education and enhance
global competitiveness, thus ensuring a high global rank and an excellent performance …
global competitiveness, thus ensuring a high global rank and an excellent performance …