Software defect number prediction: Unsupervised vs supervised methods

2019 ◽  
Vol 106 ◽  
pp. 161-181 ◽  
Author(s):  
Xiang Chen ◽  
Dun Zhang ◽  
Yingquan Zhao ◽  
Zhanqi Cui ◽  
Chao Ni
Keyword(s):  
Defect Number ◽  
Software Defect ◽  
Supervised Methods

scholarly journals Multi-project Regression based Approach for Software Defect Number Prediction

2019 ◽  
Author(s):  
Qiguo Huang ◽  
Chao Ni ◽  
Xiang Chen ◽  
Qing Gu ◽  
Kaibo Cao
Keyword(s):  
Defect Number ◽  
Software Defect

Software Defect Prediction Incremental Model using Ensemble Learning

2020 ◽  
Vol 16 (11) ◽  
pp. 1771
Author(s):  
Wang Shibo ◽  
Li Yong ◽  
Mi Wenbo ◽  
Liu Ying
Keyword(s):  
Ensemble Learning ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Incremental Model ◽  
Software Defect

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

Research of Software Defect Prediction Model Based on ACO-SVM

2011 ◽  
Vol 34 (6) ◽  
pp. 1148-1154 ◽  
Author(s):  
Hui-Yan JIANG ◽  
Mao ZONG ◽  
Xiang-Ying LIU
Keyword(s):  
Prediction Model ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Model Based ◽  
Software Defect

Software Defect Prediction via Deep Belief Network

2019 ◽  
Vol 28 (5) ◽  
pp. 925-932
Author(s):  
Hua WEI ◽  
Chun SHAN ◽  
Changzhen HU ◽  
Yu ZHANG ◽  
Xiao YU
Keyword(s):  
Deep Belief Network ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Belief Network ◽  
Software Defect

Adversarial training for few-shot text classification

2021 ◽  
Vol 14 (2) ◽  
pp. 201-214
Author(s):  
Danilo Croce ◽  
Giuseppe Castellucci ◽  
Roberto Basili
Keyword(s):  
Supervised Learning ◽  
Language Processing ◽  
Reproducing Kernel ◽  
Generative Adversarial Networks ◽  
Training Material ◽  
Semantic Classification ◽  
Universal Sentence ◽  
Kernel Hilbert Spaces ◽  
Supervised Methods ◽  
Low Dimensional

In recent years, Deep Learning methods have become very popular in classification tasks for Natural Language Processing (NLP); this is mainly due to their ability to reach high performances by relying on very simple input representations, i.e., raw tokens. One of the drawbacks of deep architectures is the large amount of annotated data required for an effective training. Usually, in Machine Learning this problem is mitigated by the usage of semi-supervised methods or, more recently, by using Transfer Learning, in the context of deep architectures. One recent promising method to enable semi-supervised learning in deep architectures has been formalized within Semi-Supervised Generative Adversarial Networks (SS-GANs) in the context of Computer Vision. In this paper, we adopt the SS-GAN framework to enable semi-supervised learning in the context of NLP. We demonstrate how an SS-GAN can boost the performances of simple architectures when operating in expressive low-dimensional embeddings; these are derived by combining the unsupervised approximation of linguistic Reproducing Kernel Hilbert Spaces and the so-called Universal Sentence Encoders. We experimentally evaluate the proposed approach over a semantic classification task, i.e., Question Classification, by considering different sizes of training material and different numbers of target classes. By applying such adversarial schema to a simple Multi-Layer Perceptron, a classifier trained over a subset derived from 1% of the original training material achieves 92% of accuracy. Moreover, when considering a complex classification schema, e.g., involving 50 classes, the proposed method outperforms state-of-the-art alternatives such as BERT.

Connecting Software Reliability Growth Models to Software Defect Tracking

2020 ◽  
Author(s):  
Maskura Nafreen ◽  
Melanie Luperon ◽  
Lance Fiondella ◽  
Vidhyashree Nagaraju ◽  
Ying Shi ◽  
...  
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Reliability Growth ◽  
Software Defect ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth

scholarly journals Real-World Person Re-Identification via Super-Resolution and Semi-Supervised Methods

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 35834-35845
Author(s):  
Limin Xia ◽  
Jiahui Zhu ◽  
Zhimin Yu
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Supervised Methods

scholarly journals A Survey on Contrastive Self-Supervised Learning

Technologies ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 2
Author(s):  
Ashish Jaiswal ◽  
Ashwin Ramesh Babu ◽  
Mohammad Zaki Zadeh ◽  
Debapriya Banerjee ◽  
Fillia Makedon
Keyword(s):  
Computer Vision ◽  
Supervised Learning ◽  
Language Processing ◽  
Large Scale ◽  
Performance Comparison ◽  
Extensive Review ◽  
Future Directions ◽  
Dominant Component ◽  
Supervised Methods ◽  
The Cost

Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.

Sign in / Sign up

Export Citation Format

Share Document