An optimized feature selection using fuzzy mutual information based ant colony optimization for software defect prediction

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    In recent years, there is a significant notification focused towards the prediction of software defect in the field of software engineering. The prediction of software defects assist in reducing the cost of testing effort, improving the process of software testing and to concentrate only on the fault-prone software modules. Recently, software defect prediction is an important research topic in the software engineering field. One of the important factors which effect the software defect detection is the presence of noisy features in the dataset. The objective of this proposed work is to contribute an optimization technique for the selection of potential features to improve the prediction capability of software defects more accurately. The Fuzzy Mutual Information Ant Colony Optimization is used for searching the optimal feature set with the ability of Meta heuristic search. This proposed feature selection efficiency is evaluated using the datasets from NASA metric data repository. Simulation results have indicated that the proposed method makes an impressive enhancement in the prediction of routine for three different classifiers used in this work.


  • Keywords


    Software Defect Prediction; Fuzzy Mutual Information; Ant Colony Optimization; Potential Features

  • References


      [1] Naresh E & Vijaya Kumar BP, “Comparative Analysis of the Various Data Mining Techniques for Defect Prediction using the NASA MDP Datasets for Better Quality of the Software Product”, Advances in Computational Sciences and Technology, Vol.10, No.7, (2017), pp.2005-2017.

      [2] PonPeriasamy AR &Mishbahulhud A, “Data Mining Techniques in Software Defect Prediction”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.7, No.3, (2017).

      [3] Paramshetti P &Phalk DA, “Software Defect Prediction for Quality Improvement Using Hybrid Approach”, International Journal of Application or Innovation in Engineering & Management, Vol.4, No.6, (2015), pp.99-104.

      [4] Shukla HS & Verma DK, “A Review on Software Defect Prediction”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol.4, No.12), pp.4387-4394.

      [5] Kumar Dwivedi V & Singh MK, “Software Defect Prediction Using Data Mining Classification Approach”, International Journal of Technical Research and Applications, Vol.4, No.6, (2016), pp.31-35.

      [6] Merugula S, “A Study on Software Defect Prediction Using Classification Techniques”, International Journal of Computer Science & Engineering Technology, Vol.7, No.11, (2016).

      [7] SatyaSrinivas M, Yesubabu A &Pradeepini G, “Feature Selection Based Neural Networks for Software Defect Prediction”, IOSR Journal of Computer Engineering, Vol.18, No.6, (2016), pp.122-125.

      [8] Laradji IH, Alshayeb M &Ghouti L, “software Defect Prediction using Ensemble Learning on Selected Features”, Information and Software Technology, Vol.58, (2015), pp.388-402. https://doi.org/10.1016/j.infsof.2014.07.005.

      [9] Wahono RS, “A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks”, Journal of Software Engineering, (2015).

      [10] Kaur A, “Defect Prediction by Pruning Redundancy in Association Rule Mining”, International Journal of Advanced Research in Computer Science, (2017).

      [11] Kaur K, “Analysis of resilient back-propogation for improving software process control”, International Journal of Information Technology and Knowledge Management, Vol.5, No.2, (2012), pp.377-379.

      [12] Adline A & Ramachandran M, “Predicting the Software Fault Using the Method of Genetic Algorithm”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.3, Special Issue 2, (2014), pp.390-398.

      [13] Karpagavadivu K, Maragatham T & Karthik S, “A Survey of Different Software Fault Prediction Using Data Mining Techniques Methods”, International Journal of Advanced Research in Computer Engineering & Technology, Vol.1, No.8, (2012), pp.1-3.

      [14] Okutan A &Yildiz OT, “A Novel Regression Method for Software Defect Prediction with Kernel Methods”, ICPRAM, (2013), pp.216-221.

      [15] Dash Y & Dubey SK, “Quality prediction in object oriented system by using ANN: a brief survey”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.2, No.2, (2012).

      [16] Kaur MPJ & Pallavi M, Data mining techniques for software defect prediction. International Journal of Software and Web Sciences (IJSWS), Vol.3, No.1, (2013), pp.54-57.

      [17] Yang X, Tang K & Yao X, “A learning-to-rank approach to software defect prediction”, IEEE Transactions on Reliability, Vol.64, No.1, (2015), pp.234-246.https://doi.org/10.1109/TR.2014.2370891.

      [18] Khushaba RN, Kodagoda S, Lal S & Dissanayake G, “Driver drowsiness classification using fuzzy wavelet packet based feature extraction algorithm”, IEEE Transactions on Biomedical Engineering, Vol.58, No.1, (2011), pp.121-131.https://doi.org/10.1109/TBME.2010.2077291.

      [19] http://promise.site.uottawa.ca/SERepository


 

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Article ID: 9954
 
DOI: 10.14419/ijet.v7i1.1.9954




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