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An ANN Based Approach for Software Fault Prediction Using Object Oriented Metrics

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Book cover Advanced Informatics for Computing Research (ICAICR 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 955))

Abstract

During recent years, the enormous increase in demand for software products has been experienced. High quality software is the major demand of users. Predicting the faults in early stages will improve the quality of software and apparently reduce the development efforts or cost. Fault prediction is majorly based on the selection of technique and the metrics to predict the fault. Thus metrics selection is a critical part of software fault prediction. Currently techniques been evaluated based on traditional set of metrics. There is a need to identify the different techniques and evaluate them on the bases of appropriate metrics. In this research, Artificial neural network is used. For classification task, ANN is one of the most effective technique. Artificial neural network based SFP model is designed for classification in this study. Prediction is performed on the basis of object-oriented metrics. 5 object oriented metrics from CK and Martin metric sets are selected as input parameters. The experiments are performed on 18 public datasets from PROMISE repository. Receiver operating characteristíc curve, accuracy, and Mean squared error are taken as performance parameters for the prediction task. Results of the proposed systems signify that ANN provides significant results in terms of accuracy and error rate.

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Authors and Affiliations

  1. Department of Computer Science Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India

    Rajdeep Kaur & Sumit Sharma

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  1. Rajdeep Kaur
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  2. Sumit Sharma
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Correspondence to Rajdeep Kaur .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, Haryana, India

    Ashish Kumar Luhach

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  3. National Chung Cheng University, Minxiong Township, Chiayi County, Taiwan

    Pao-Ann Hsiung

  4. Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  5. Saint Mary's University, Halifax, NS, Canada

    Pawan Lingras

  6. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

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Kaur, R., Sharma, S. (2019). An ANN Based Approach for Software Fault Prediction Using Object Oriented Metrics. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 955. Springer, Singapore. https://doi.org/10.1007/978-981-13-3140-4_31

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  • DOI: https://doi.org/10.1007/978-981-13-3140-4_31

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