short-paper

On Applicability of Cross-project Defect Prediction Method for Multi-Versions Projects

Author:

PROMISE: Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering

Pages 93 - 96

https://doi.org/10.1145/3127005.3127015

Published: 08 November 2017 Publication History

Get Access

Abstract

Context: Cross-project defect prediction (CPDP) research has been popular, and many CPDP methods have been proposed so far. As the straightforward use of Cross-project (CP) data was useless, those methods filter, weigh, and adapt CP data for a target project data. This idea would also be useful for a project having past defect data. Objective: To evaluate the applicability of CPDP methods for multi-versions projects. The evaluation focused on the relationship between the performance change and the proximity of older release data to a target project. Method: We conducted experiments that compared the predictive performance between using older version data with and without Nearest Neighbor (NN) filter, a classic CPDP method. Results: NN-filter could not make clear differences in predictive performance. Conclusions: NN-filter was not helpful for improving predictive performance with older release data.

References

[1]

Lin Chen, Bin Fang, Zhaowei Shang, and Yuanyan Tang. 2015. Negative samples reduction in cross-company software defects prediction. Info. and Softw. Technol. 62, C (2015), 67--77.

Digital Library

Google Scholar

[2]

Steffen Herbold. 2013. Training data selection for cross-project defect prediction. In Proc. of PROMISE '13. ACM, 6:1--6:10.

Digital Library

Google Scholar

[3]

Marian Jureczko and Lech Madeyski. 2010. Towards identifying software project clusters with regard to defect prediction. In Proc. of PROMISE '10. ACM, 9:1--9:10.

Digital Library

Google Scholar

[4]

Ying Ma, Guangchun Luo, Xue Zeng, and Aiguo Chen. 2012. Transfer learning for cross-company software defect prediction. Info. and Softw. Technol. 54, 3 (2012), 248--256.

Digital Library

Google Scholar

[5]

Jaechang Nam, S J Pan, and Sunghun Kim. 2013. Transfer defect learning. In Proc. of ICSE 2013. IEEE, 382--391.

Crossref

Google Scholar

[6]

Fayola Peters, Tim Menzies, and Andrian Marcus. 2013. Better cross company defect prediction. In Proc. of MSR 2013. IEEE, 409--418.

Crossref

Google Scholar

[7]

Foyzur Rahman, Daryl Posnett, and Premkumar Devanbu. 2012. Recalling the "imprecision" of cross-project defect prediction. In Proc. of ESEC/FSE '12. ACM, 61:1--61:11.

Digital Library

Google Scholar

[8]

Rakesh Rana, Miroslaw Staron, Christian Berger, Jörgen Hansson, Martin Nilsson, and Wilhelm Meding. 2014. The Adoption of Machine Learning Techniques for Software Defect Prediction: An Initial Industrial Validation. In Proc. of Joint Conference on Knowledge-based Software Engineering. 270--285.

Crossref

Google Scholar

[9]

Ayşe Tosun, Ayşe Bener, Burak Turhan, and Tim Menzies. 2010. Practical considerations in deploying statistical methods for defect prediction: A case study within the Turkish telecommunications industry. Info. and Softw. Technol. 52, 11 (2010), 1242--1257.

Digital Library

Google Scholar

[10]

Burak Turhan, Tim Menzies, Ayşe B Bener, and Justin Di Stefano. 2009. On the relative value of cross-company and within-company data for defect prediction. Empir. Softw. Eng. 14, 5 (2009), 540--578.

Digital Library

Google Scholar

Cited By

View all

Suhag VDubey SSharma B(2024)Software defect prediction using global and local modelsInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02407-715:8(4003-4017)Online publication date: 2-Jul-2024
https://doi.org/10.1007/s13198-024-02407-7
Kabir MBegum SAhmed MRehman A(2022)CODE: A Moving-Window-Based Framework for Detecting Concept Drift in Software Defect PredictionSymmetry10.3390/sym1412250814:12(2508)Online publication date: 28-Nov-2022
https://doi.org/10.3390/sym14122508
Yu QZhu YHan HZhao YJiang SQian J(2022)Evolutionary Measures for Object-oriented Projects and Impact on the Performance of Cross-version Defect PredictionProceedings of the 13th Asia-Pacific Symposium on Internetware10.1145/3545258.3545275(192-201)Online publication date: 11-Jun-2022
https://dl.acm.org/doi/10.1145/3545258.3545275
Show More Cited By

Index Terms

On Applicability of Cross-project Defect Prediction Method for Multi-Versions Projects
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis

Recommendations

Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
ESEC/FSE '09: Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering

Prediction of software defects works well within projects as long as there is a sufficient amount of data available to train any models. However, this is rarely the case for new software projects and for many companies. So far, only a few have studies ...
An investigation on the feasibility of cross-project defect prediction

Software defect prediction helps to optimize testing resources allocation by identifying defect-prone modules prior to testing. Most existing models build their prediction capability based on a set of historical data, presumably from the same or similar ...
How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction

Background. Recent years have seen an increasing interest in cross-project defect prediction (CPDP), which aims to apply defect prediction models built on source projects to a target project. Currently, a variety of (complex) CPDP models have been ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

PROMISE: Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering

November 2017

120 pages

ISBN:9781450353052

DOI:10.1145/3127005

General Chair:
Burak Turhan,
Program Chairs:
David Bowes,
Emad Shihab

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: 08 November 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

Japan Society for the Promotion of Science

Conference

PROMISE

PROMISE: Predictive Modelling in Software Engineering

November 8, 2017

Toronto, Canada

Acceptance Rates

PROMISE Paper Acceptance Rate 12 of 25 submissions, 48%;

Overall Acceptance Rate 98 of 213 submissions, 46%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
123
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Suhag VDubey SSharma B(2024)Software defect prediction using global and local modelsInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02407-715:8(4003-4017)Online publication date: 2-Jul-2024
https://doi.org/10.1007/s13198-024-02407-7
Kabir MBegum SAhmed MRehman A(2022)CODE: A Moving-Window-Based Framework for Detecting Concept Drift in Software Defect PredictionSymmetry10.3390/sym1412250814:12(2508)Online publication date: 28-Nov-2022
https://doi.org/10.3390/sym14122508
Yu QZhu YHan HZhao YJiang SQian J(2022)Evolutionary Measures for Object-oriented Projects and Impact on the Performance of Cross-version Defect PredictionProceedings of the 13th Asia-Pacific Symposium on Internetware10.1145/3545258.3545275(192-201)Online publication date: 11-Jun-2022
https://dl.acm.org/doi/10.1145/3545258.3545275
Wang SWang JNam JNagappan NLanubile F(2021)Continuous Software Bug PredictionProceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1145/3475716.3475790(1-12)Online publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1145/3475716.3475790
Fiore ARusso AGravino CRisi M(2021) Combining CNN with DS 3 for Detecting Bug-prone Modules in Cross-version Projects 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA53835.2021.00021(91-98)Online publication date: Sep-2021
https://doi.org/10.1109/SEAA53835.2021.00021
Bennin KAli NBorstler JYu X(2020)Revisiting the Impact of Concept Drift on Just-in-Time Quality Assurance2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS51102.2020.00020(53-59)Online publication date: Dec-2020
https://doi.org/10.1109/QRS51102.2020.00020
Suhag VGarg ADubey SSharma B(2020)Analytical Approach to Cross Project Defect PredictionSoft Computing: Theories and Applications10.1007/978-981-15-0751-9_66(713-736)Online publication date: 11-Feb-2020
https://doi.org/10.1007/978-981-15-0751-9_66
Chen XMu YQu YNi CLiu MHe TLiu S(2020)Do different cross‐project defect prediction methods identify the same defective modules?Journal of Software: Evolution and Process10.1002/smr.223432:5Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1002/smr.2234
Sohan MJabiullah MRahman SMahmud S(2019)Assessing the Effect of Imbalanced Learning on Cross-project Software Defect Prediction2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT45670.2019.8944622(1-6)Online publication date: Jul-2019
https://doi.org/10.1109/ICCCNT45670.2019.8944622
Amasaki STurhan BTosun AMcIntosh S(2018)Cross-Version Defect Prediction using Cross-Project Defect Prediction ApproachesProceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3273934.3273938(32-41)Online publication date: 10-Oct-2018
https://dl.acm.org/doi/10.1145/3273934.3273938
Show More Cited By

View Options

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

Cross-project defect prediction: a large scale experiment on data vs. domain vs. process

An investigation on the feasibility of cross-project defect prediction

How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction