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Sahand: A Software Fault-Prediction Method Using Autoencoder Neural Network and K-Means Algorithm

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Abstract

Software is playing a growing role in many safety-critical applications, and software systems dependability is a major concern. Predicting faulty modules of software before the testing phase is one method for enhancing software reliability. The ability to predict and identify the faulty modules of software can lower software testing costs. Machine learning algorithms can be used to solve software fault prediction problem. Identifying the faulty modules of software with the maximum accuracy, precision, and performance are the main objectives of this study. A hybrid method combining the autoencoder and the K-means algorithm is utilized in this paper to develop a software fault predictor. The autoencoder algorithm, as a preprocessor, is used to select the effective attributes of the training dataset and consequently to reduce its size. Using an autoencoder with the K-means clustering method results in lower clustering error and time. Tests conducted on the standard NASA PROMIS data sets demonstrate that by removing the inefficient elements from the training data set, the proposed fault predictor has increased accuracy (96%) and precision (93%). The recall criteria provided by the proposed method is about 87%. Also, reducing the time necessary to create the software fault predictor is the other merit of this study.

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Data Availability

Access.

The data relating to the current study is available via the following link:

https://drive.google.com/drive/folders/1-aX_QueAUV1PhL9rBOAFn0ZzS5RcnNXF? usp=drive_link.

Notes

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Author information

Authors and Affiliations

  1. Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey

    Bahman Arasteh

  2. Applied Science Research Center, Applied Science Private University, Amman, Jordan

    Bahman Arasteh

  3. Department of Software Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Sahar Golshan

  4. Department of Software Engineering, Seraj Institute, Tabriz, Azerbaijan Province, Iran

    Shiva Shami

  5. Data Science Application and Research Center (VEBIM), Fatih Sultan Mehmet Vakif University, Istanbul, Turkey

    Farzad Kiani

Authors
  1. Bahman Arasteh
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  2. Sahar Golshan
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  3. Shiva Shami
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  4. Farzad Kiani
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Contributions

The proposed method was developed and discretized by B. Arasteh and S. Golshani. The designed algorithm was implemented and coded by B. Arasteh and S. Shami. The implemented method code was adapted and benchmarked by B. Arasteh. The data and results analysis were performed by B. Arasteh and S. Golshani. The manuscript of the paper was written by B. Arasteh and F. Kiani.

Corresponding author

Correspondence to Bahman Arasteh.

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Ethical and Informed Consent for data used

The data used in this research does not belong to any other person or third party and was prepared and generated by the researchers themselves during the research. The data of this research will be accessible to other researchers.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial conflict of interest.

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Responsible Editor: Y. Malaiya.

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Arasteh, B., Golshan, S., Shami, S. et al. Sahand: A Software Fault-Prediction Method Using Autoencoder Neural Network and K-Means Algorithm. J Electron Test 40, 229–243 (2024). https://doi.org/10.1007/s10836-024-06116-8

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  • DOI: https://doi.org/10.1007/s10836-024-06116-8

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