research-article

Software code analysis using ensemble learning techniques

Author:

Simran AggarwalAuthors Info & Claims

AISS '19: Proceedings of the 1st International Conference on Advanced Information Science and System

Article No.: 9, Pages 1 - 7

https://doi.org/10.1145/3373477.3373486

Published: 15 January 2020 Publication History

Get Access

Abstract

Ensuing the advent of advancements in software systems, the probability of them containing high severity defects is exponentially on the rise. With each technological addition, the complexity of software is increasing. Reproduction and rectification of a defect requires time and effort. Current state of the art analysis tools cater to the investigation of static aspects of a production level code. However, it is imperative to assess the dynamic development process of a system so as to be able to timely detect erroneous components early on in the development life cycle of a software. A novel automated defect prediction feature enhancement is proposed that analyses the static structure of the current code and state of the software in past releases to extract relevant static and dynamic feature sets. Data generated is modelled for defect trends in the future release of the software by four ensemble classifiers. Results demonstrate the superiority of Voting algorithm for the problem of defect prediction.

References

[1]

R. Malhotra, S. Shukla, and G. Sawhney. Assessment of defect prediction models using machine learning techniques for object-oriented systems. In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pages 577--583, Sep. 2016.

Crossref

Google Scholar

[2]

T. Gyimothy, R. Ferenc, and I. Siket. Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions on Software Engineering, 31(10):897--910, Oct 2005.

Digital Library

Google Scholar

[3]

S. R. Chidamber and C. F. Kemerer. A metrics suite for object oriented design. IEEE Transactions on Software Engineering, 20(6):476--493, June 1994.

Digital Library

Google Scholar

[4]

Yuming Zhou and Hareton Leung. Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Transactions on Software Engineering, 32(10):771--789, Oct 2006.

Digital Library

Google Scholar

[5]

Yogesh Singh, Arvinder Kaur, and Ruchika Malhotra. Empirical validation of object-oriented metrics for predicting fault proneness models. Software Quality Journal, 18(1):3--35, March 2010.

Digital Library

Google Scholar

[6]

Ruchika Malhotra, Arvinder Kaur, and Yogesh Singh. Empirical validation of object-oriented metrics for predicting fault proneness at different severity levels using support vector machines. International Journal of System Assurance Engineering and Management, 1:269--281, 09 2010.

Crossref

Google Scholar

[7]

G. J. Pai and J. Bechta Dugan. Empirical analysis of software fault content and fault proneness using bayesian methods. IEEE Transactions on Software Engineering, 33(10):675--686, Oct 2007.

Digital Library

Google Scholar

[8]

Erik Arisholm, Lionel Briand, and Eivind B. Johannessen. A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. Journal of Systems and Software, 83:2--17, 01 2010.

Digital Library

Google Scholar

[9]

C. Catal, B. Diri, and B. Ozumut. An artificial immune system approach for fault prediction in object-oriented software. In 2nd International Conference on Dependability of Computer Systems (DepCoS-RELCOMEX '07), pages 238--245, June 2007.

Digital Library

Google Scholar

[10]

N Kalaivani and R Beena. Overview of software defect prediction using machine learning algorithms. International Journal of Pure and Applied Mathematics, 118(20):3863--3873, 2018.

Google Scholar

[11]

Peng He, Bing Li, Xiao Liu, Jun Chen, and Yutao Ma. An empirical study on software defect prediction with simplified metric set. Information and Software Technology, 59:170--190, 02 2014.

Digital Library

Google Scholar

[12]

Logan Perreault, Seth Berardinelli, Clemente Izurieta, and John Sheppard.

Google Scholar

[13]

A. Chug and S. Dhall. Software defect prediction using supervised learning algorithm and unsupervised learning algorithm. In Confluence 2013: The Next Generation Information Technology Summit (4th International Conference), pages 173--179, Sep. 2013.

Crossref

Google Scholar

Cited By

View all

Idialu OMathews NMaipradit RAtlee JNagappan MSpinellis DConstantinou EBacchelli A(2024)Whodunit: Classifying Code as Human Authored or GPT-4 Generated - A case study on CodeChef problemsProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644926(394-406)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644926
Sharma TKechagia MGeorgiou STiwari RVats IMoazen HSarro F(2024)A survey on machine learning techniques applied to source codeJournal of Systems and Software10.1016/j.jss.2023.111934209:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111934
Olivares-Galindo JSánchez-García ÁBarrientos-Martínez ROcharán-Hernández J(2023)Ensemble Classifiers in Software Defect Prediction: A Systematic Literature Review2023 11th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT58849.2023.00011(1-8)Online publication date: 6-Nov-2023
https://doi.org/10.1109/CONISOFT58849.2023.00011

Index Terms

Software code analysis using ensemble learning techniques
1. Computing methodologies
  1. Machine learning
    1. Cross-validation
    2. Machine learning algorithms
      1. Ensemble methods
        Bagging
        Boosting
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis

Recommendations

An empirical comparison of machine learning techniques for software defect prediction
BICT '14: Proceedings of the 8th International Conference on Bioinspired Information and Communications Technologies

Software systems are exposed to various types of defects. The timely identification of defective classes is essential in early phases of software development to reduce the cost of testing the software. This will guide the software practitioners and ...
A new metric for predicting software change using gene expression programming
WETSoM 2014: Proceedings of the 5th International Workshop on Emerging Trends in Software Metrics

Software metrics help in determining the quality of a software product. They can be used for continuous inspection of a software to assist software developers in improving its quality. We can also use metrics to develop quality models which predict ...
Software change prediction: a literature review

In industrial organisations, software products are quite large and complex, consisting of a number of classes. Thus, it is not possible to test all the products with a finite number of resources. Hence, it would be beneficial if we could predict in ...

Comments

Information & Contributors

Information

Published In

AISS '19: Proceedings of the 1st International Conference on Advanced Information Science and System

November 2019

253 pages

ISBN:9781450372916

DOI:10.1145/3373477

Conference Chairs:
Alexander Balinsky
Cardiff University, UK
,
Ah-Hwee Tan
Nanyang Technological University, Singapore
,
Wernhuar Tarng
National Tsing Hua University, Taiwan

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 January 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

AISS 2019

AISS 2019: 2019 International Conference on Advanced Information Science and System

November 15 - 17, 2019

Singapore, Singapore

Acceptance Rates

AISS '19 Paper Acceptance Rate 41 of 95 submissions, 43%;

Overall Acceptance Rate 41 of 95 submissions, 43%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
109
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Idialu OMathews NMaipradit RAtlee JNagappan MSpinellis DConstantinou EBacchelli A(2024)Whodunit: Classifying Code as Human Authored or GPT-4 Generated - A case study on CodeChef problemsProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644926(394-406)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644926
Sharma TKechagia MGeorgiou STiwari RVats IMoazen HSarro F(2024)A survey on machine learning techniques applied to source codeJournal of Systems and Software10.1016/j.jss.2023.111934209:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111934
Olivares-Galindo JSánchez-García ÁBarrientos-Martínez ROcharán-Hernández J(2023)Ensemble Classifiers in Software Defect Prediction: A Systematic Literature Review2023 11th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT58849.2023.00011(1-8)Online publication date: 6-Nov-2023
https://doi.org/10.1109/CONISOFT58849.2023.00011

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

An empirical comparison of machine learning techniques for software defect prediction

A new metric for predicting software change using gene expression programming

Software change prediction: a literature review

Comments

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

An empirical comparison of machine learning techniques for software defect prediction

A new metric for predicting software change using gene expression programming

Software change prediction: a literature review

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations