An Efficient Approach to Predict Software Defect - Review

Authors

  • Vanita Patni  Department of Computer Engineering, PIET, Baroda, Gujarat, India
  • Divyesh Joshi  Department of Computer Engineering, PIET, Baroda, Gujarat, India

Keywords:

Software defect prediction, graph-based clustering

Abstract

Faults in software systems are a major problem. A software fault is a defect that causes software failure in an executable product. Quality of a product is correlated with the number of defects as well as it is limited by time and by money. The possibility of early estimating the potential faultiness of software could help on planning, controlling and executing software development activities. So, defect prediction is very important in the field of software quality and software reliability. With the increase of the web software complexity, defect detection and prevention have become crucial processes in the software industry. It is applied to web based systems using graph-based clustering algorithms. An appropriate implementation of the graph-based clustering in defect prediction may facilitate to estimate defects in a web page source code.

References

  1. Muhammed Maruf Ozturk, Unal Cavusoglu, Ahmet Zengin, A novel defect prediction method for web pages using k-means++, ELSEVIER 2015
  2. Ahmed H. Yousef, Extracting software static defect models using data minig, Elsevier , 2015.
  3. Pamela Bhattacharya, Marios Iliofotou, Iulian  Neamtiu, Michalis Faloutsos,Graph Based Analysis and predictionforsoftwareEvolution,IEEE2012
  4. Ishani Aroraa, Vivek Tetarwala, Anju Sahaa , Open issues in software defect prediction,Elsevier, 2015
  5. A Systematic Literacture Review  of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks,JournalOfSoftwareEngineering,April2015.
  6. Golnoush Abaei · Ali Selamat, A survey on software fault detection based on different prediction approaches, springer, 2014
  7. Luis A. Leiva, Enrique Vidal, Warped K-Means: An Algorithm to cluster sequentially-distributed data, Elsevier, 2013
  8. Bahman Bahmani, Benjamin Moseley, Andrea Vattani, Ravi kumar, Sergei Vassilvitskii, Scalable K-Means++, VLDB Endowment, 2012
  9. Ali Hadian, Saeed Shahrivari,High performance parallel k-means clustering for disk-resident datasets on multi-core CPUs, Springer, 2014
  10. Hesham Abandah, Izzat Alsmadi, Call Graph Based Metrics To   Evaluate Software Design Quality, International journal of software Engineering and Its Applications, January 2013.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2016-06-30

Issue

Volume 2, Issue 3, May-June-2016

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vanita Patni, Divyesh Joshi "An Efficient Approach to Predict Software Defect - Review" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 3, pp.80-82, May-June-2016.