A software defect detection algorithm based on asymmetric classification evaluation

Yanqi Xie; Xirong Lv; Ying Jiang; Zanyou Su; Nan Luo; Ying Ma

doi:10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00232

A software defect detection algorithm based on asymmetric classification evaluation

Yanqi Xie
, Xirong Lv
, Ying Jiang
, Zanyou Su
, Nan Luo
, Ying Ma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper proposes a software defect detection algorithm based on asymmetric classification evaluation, and applies the algorithm to a complete system. The system includes software data input interface, controller and detection result output port. The controller is used to detect the received software module, obtain the original software measurement data set, and calculate the number of the original software measurement data set. According to the data preprocessing, the data is divided into training samples and test samples. The training sample data is classified into asymmetric model. Discriminated and structured dictionary is used to evaluate the performance of asymmetric classifier. The detection is transferred to the test samples, the model is used to detect the defects of the software detection module, the evaluation results are fed back to the tester to complete the detection; and then through the detection results, If the detection result is provided to the user. The algorithm proposed in this paper can enhance the representation ability of dictionary and has good discrimination performance. At the same time, it can effectively solve the error caused by the imbalance of data and accurately locate the location of software defects.

Original language	English
Title of host publication	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1724-1728
Number of pages	5
ISBN (Electronic)	9781665435741
DOIs	https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00232
State	Published - 2021
Externally published	Yes
Event	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 - New York, United States Duration: 30 Sep 2021 → 3 Oct 2021

Publication series

Name	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

Conference

Conference	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
Country/Territory	United States
City	New York
Period	30/09/21 → 3/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Asymmetric evaluation
Machine learning
Software defect detection

Access to Document

10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00232

Cite this

Xie, Y., Lv, X., Jiang, Y., Su, Z., Luo, N., & Ma, Y. (2021). A software defect detection algorithm based on asymmetric classification evaluation. In 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 (pp. 1724-1728). (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00232

Xie, Yanqi ; Lv, Xirong ; Jiang, Ying et al. / A software defect detection algorithm based on asymmetric classification evaluation. 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1724-1728 (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021).

@inproceedings{63e553ae37f0470d807d2833b5e987bc,

title = "A software defect detection algorithm based on asymmetric classification evaluation",

abstract = "This paper proposes a software defect detection algorithm based on asymmetric classification evaluation, and applies the algorithm to a complete system. The system includes software data input interface, controller and detection result output port. The controller is used to detect the received software module, obtain the original software measurement data set, and calculate the number of the original software measurement data set. According to the data preprocessing, the data is divided into training samples and test samples. The training sample data is classified into asymmetric model. Discriminated and structured dictionary is used to evaluate the performance of asymmetric classifier. The detection is transferred to the test samples, the model is used to detect the defects of the software detection module, the evaluation results are fed back to the tester to complete the detection; and then through the detection results, If the detection result is provided to the user. The algorithm proposed in this paper can enhance the representation ability of dictionary and has good discrimination performance. At the same time, it can effectively solve the error caused by the imbalance of data and accurately locate the location of software defects.",

keywords = "Asymmetric evaluation, Machine learning, Software defect detection",

author = "Yanqi Xie and Xirong Lv and Ying Jiang and Zanyou Su and Nan Luo and Ying Ma",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 ; Conference date: 30-09-2021 Through 03-10-2021",

year = "2021",

doi = "10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00232",

language = "英语",

series = "19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1724--1728",

booktitle = "19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021",

address = "美国",

}

Xie, Y, Lv, X, Jiang, Y, Su, Z, Luo, N & Ma, Y 2021, A software defect detection algorithm based on asymmetric classification evaluation. in 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1724-1728, 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021, New York, United States, 30/09/21. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00232

A software defect detection algorithm based on asymmetric classification evaluation. / Xie, Yanqi; Lv, Xirong; Jiang, Ying et al.
19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1724-1728 (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A software defect detection algorithm based on asymmetric classification evaluation

AU - Xie, Yanqi

AU - Lv, Xirong

AU - Jiang, Ying

AU - Su, Zanyou

AU - Luo, Nan

AU - Ma, Ying

PY - 2021

Y1 - 2021

N2 - This paper proposes a software defect detection algorithm based on asymmetric classification evaluation, and applies the algorithm to a complete system. The system includes software data input interface, controller and detection result output port. The controller is used to detect the received software module, obtain the original software measurement data set, and calculate the number of the original software measurement data set. According to the data preprocessing, the data is divided into training samples and test samples. The training sample data is classified into asymmetric model. Discriminated and structured dictionary is used to evaluate the performance of asymmetric classifier. The detection is transferred to the test samples, the model is used to detect the defects of the software detection module, the evaluation results are fed back to the tester to complete the detection; and then through the detection results, If the detection result is provided to the user. The algorithm proposed in this paper can enhance the representation ability of dictionary and has good discrimination performance. At the same time, it can effectively solve the error caused by the imbalance of data and accurately locate the location of software defects.

AB - This paper proposes a software defect detection algorithm based on asymmetric classification evaluation, and applies the algorithm to a complete system. The system includes software data input interface, controller and detection result output port. The controller is used to detect the received software module, obtain the original software measurement data set, and calculate the number of the original software measurement data set. According to the data preprocessing, the data is divided into training samples and test samples. The training sample data is classified into asymmetric model. Discriminated and structured dictionary is used to evaluate the performance of asymmetric classifier. The detection is transferred to the test samples, the model is used to detect the defects of the software detection module, the evaluation results are fed back to the tester to complete the detection; and then through the detection results, If the detection result is provided to the user. The algorithm proposed in this paper can enhance the representation ability of dictionary and has good discrimination performance. At the same time, it can effectively solve the error caused by the imbalance of data and accurately locate the location of software defects.

KW - Asymmetric evaluation

KW - Machine learning

KW - Software defect detection

UR - https://www.scopus.com/pages/publications/85124166151

U2 - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00232

DO - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00232

M3 - 会议稿件

AN - SCOPUS:85124166151

T3 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

SP - 1724

EP - 1728

BT - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

Y2 - 30 September 2021 through 3 October 2021

ER -

Xie Y, Lv X, Jiang Y, Su Z, Luo N, Ma Y. A software defect detection algorithm based on asymmetric classification evaluation. In 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1724-1728. (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021). doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00232

A software defect detection algorithm based on asymmetric classification evaluation

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this