Analysis of Data mining based Software Defect Prediction Techniques

α
Naheed Azeem
Naheed Azeem
σ
Shazia Usmani
Shazia Usmani
α Federal Urdu University Federal Urdu University

Send Message

To: Author

Analysis of Data mining based Software Defect Prediction Techniques

Article Fingerprint

ReserarchID

69MHO

Analysis of Data mining based Software Defect Prediction Techniques Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

Abstract

Software bug repository is the main resource for fault prone modules. Different data mining algorithms are used to extract fault prone modules from these repositories. Software development team tries to increase the software quality by decreasing the number of defects as much as possible. In this paper different data mining techniques are discussed for identifying fault prone modules as well as compare the data mining algorithms to find out the best algorithm for defect prediction.

References

23 Cites in Article
  1. Erik Arisholm,Lionel Briand,Magnus Fuglerud (2011). Data Mining Techniques for Building Fault-proneness Models in Telecom Java Software.
  2. L Guo,Y Ma,B Cukic,H Singh (2004). Robust prediction of fault-proneness by random forests.
  3. Lianfa Li,Hareton Leung (2009). Using the Number of Faults to Improve Fault-Proneness Prediction of the Probability Models.
  4. (2008). Metrics for Module Defects Identification.
  5. N Nagwani,S Verma (2010). Predictive Data Mining Model for Software Bug Estimation Using Average Weighted Similarity.
  6. N Gayatri,S Nickolas,A Reddy,R Chitra (2009). Performance Analysis of Data Mining Algorithms for Software Quality Prediction.
  7. Pradeep Singh,Shirish Verma (2009). An Investigation of the Effect of Discretization on Defect Prediction Using Static Measures.
  8. Peng Huang,Jie Zhu (2009). Predicting Defect-Prone Software Modules at Different Logical Levels.
  9. Parvinder Sandhu,Raman Goel,Amanpreet Brar,Jagdeep Kaur,Sanyam Anand (2010). A model for early prediction of faults in software systems.
  10. P Singh (2009). Comparing the effectiveness of machine learning algorithms for defect prediction.
  11. Rudolf Ramler,Stefan Larndorfer,Thomas Natschläger (2009). What Software Repositories Should Be Mined for Defect Predictors?.
  12. S Shafi,S Hassan,A Arshaq,M Khan,S Shamail (2008). Software quality prediction techniques: A comparative analysis.
  13. Shivkumar Shivaji,E Whitehead,Ram Akella,Sunghun Kim (2009). Reducing Features to Improve Bug Prediction.
  14. Taghi Khoshgoftaar,Pierre Rebours,Naeem Seliya (2009). Software quality analysis by combining multiple projects and learners.
  15. T Mende,R Koschke (2009). Revisiting the Evaluation of Defect Prediction Models.
  16. Tim Menzies,Burak Turhan,Ayse Bener,Gregory Gay,Bojan Cukic,Yue Jiang (2008). Implications of ceiling effects in defect predictors.
  17. V Challagulla,F Bastani,I-Ling Yen,R Paul (2005). Empirical Assessment of Machine Learning based Software Defect Prediction Techniques.
  18. X Zhao,Y Liu,S Yong (2009). Predicting Software Defects using Multiple Criteria Linear Programming.
  19. Y Chen,X Shen,P Du,B Ge (2010). Research on software defect prediction based on data mining.
  20. Y Jiang,B Cukic,T Menzies (2007). Fault Prediction using Early Lifecycle Data.
  21. Y Jiang,B Cukic Andt,Menzies (2008). Can data transformation help in the detection of fault-prone modules?.
  22. Yasutaka Kamei,Akito Monden,Shinsuke Matsumoto,Takeshi Kakimoto,Ken-Ichi Matsumoto (2007). The Effects of Over and Under Sampling on Fault-prone Module Detection.
  23. Zhan Li,Marek Reformat (2007). A practical method for the software fault-prediction.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Naheed Azeem. 1970. \u201cAnalysis of Data mining based Software Defect Prediction Techniques\u201d. Unknown Journal GJCST Volume 11 (GJCST Volume 11 Issue 16): .

Download Citation

Journal Specifications
Version of record

v1.2

Issue date

September 7, 2011

Language
en
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 21166
Total Downloads: 11011
2026 Trends
Related Research

Published Article

Software bug repository is the main resource for fault prone modules. Different data mining algorithms are used to extract fault prone modules from these repositories. Software development team tries to increase the software quality by decreasing the number of defects as much as possible. In this paper different data mining techniques are discussed for identifying fault prone modules as well as compare the data mining algorithms to find out the best algorithm for defect prediction.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Analysis of Data mining based Software Defect Prediction Techniques

Naheed Azeem
Naheed Azeem Federal Urdu University
Shazia Usmani
Shazia Usmani

Research Journals