Sökning: WFRF:(Havulinna Aki S.)
> (2010-2014) >
A Study on ML-Based...
A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications
-
- Mcmurray, S. (författare)
- Jönköping University,Tekniska Högskolan,Department of Computer Science, Kristianstad University, Kristianstad, SE-29188, Sweden
-
- Sodhro, Ali (författare)
- Department of Computer Science, Kristianstad University, Kristianstad, SE-29188, Sweden,Research environment of Computer science,Faculty of Natural Science,Avdelningen för datavetenskap,Fakulteten för naturvetenskap
-
(creator_code:org_t)
- 2023-03-26
- 2023
- Engelska.
-
Ingår i: Sensors. - : MDPI. - 1424-8220 .- 1424-3210. ; 23:7
- Relaterad länk:
-
https://doi.org/10.3...
-
visa fler...
-
https://researchport... (primary) (free)
-
https://urn.kb.se/re...
-
https://doi.org/10.3...
-
visa färre...
Abstract
Ämnesord
Stäng
- Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Algorithms
- Bayes Theorem
- Machine Learning
- Neural Networks
- Computer
- Software
- Support Vector Machine
- Adaptive boosting
- Application programs
- Decision trees
- Defects
- Extraction
- Learning systems
- Least squares approximations
- Life cycle
- Nearest neighbor search
- Principal component analysis
- Software design
- Support vector machines
- Elastic net
- Ensemble learning
- Features extraction
- Features selection
- Machine-learning
- Partial least square regression
- Principal-component analysis
- Selection techniques
- Software defect prediction
- Software development life-cycle
- algorithm
- Feature extraction
- feature selection
- software defects prediction
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
-
Sensors
(Sök värdpublikationen i LIBRIS)
Till lärosätets databas