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What Strokes to Modify in the Painting? Code Changes Prediction for Object-Oriented Software

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Software Analysis, Testing, and Evolution (SATE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11293))

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Abstract

Software systems shall evolve to fulfill users’ increasingly various and sophisticated needs. As they become larger and more complex, the corresponding testing and maintenance have become a practical research challenge. In this paper, we employ an approach that can identify the change-proneness in the source code of new object-oriented software releases and predict the corresponding change sizes. We first define two metrics, namely Class Change Metric and Change Size Metric, to describe the features and sizes of code changes. A new software release may be based on several previous releases. Thus, we employ an Entropy Weight Method to calculate the best window size for determining the number of previous releases to use in the prediction of change-proneness in the new release. Based on a series of change evolution matrices, a code change prediction approach is proposed based on the Gauss Process Regression (GPR) algorithm. Experiments are conducted on 17 software systems collected from GitHub to evaluate our prediction approach. The results show that our approach outperforms three existing state-of-the-art approaches with significantly higher prediction accuracy.

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  1. 1.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China grants 61572350 and the National Key R&D Program of China grant NO.2017YF-B1401201.

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Authors and Affiliations

  1. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, 300072, China

    Dinan Zhang, Shizhan Chen & Zhiyong Feng

  2. School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Dinan Zhang & Shizhan Chen

  3. School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, VIC, 3783, Australia

    Qiang He

  4. School of Computer Software, Tianjin University, Tianjin, 300072, China

    Zhiyong Feng

  5. Sloan School of Management, MIT, Cambridge, MA, 02142, USA

    Keman Huang

Authors
  1. Dinan Zhang
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  2. Shizhan Chen
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  3. Qiang He
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  5. Keman Huang
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Corresponding author

Correspondence to Qiang He .

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Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Lei Bu

  2. Peking University, Peking, China

    Yingfei Xiong

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Zhang, D., Chen, S., He, Q., Feng, Z., Huang, K. (2018). What Strokes to Modify in the Painting? Code Changes Prediction for Object-Oriented Software. In: Bu, L., Xiong, Y. (eds) Software Analysis, Testing, and Evolution. SATE 2018. Lecture Notes in Computer Science(), vol 11293. Springer, Cham. https://doi.org/10.1007/978-3-030-04272-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-04272-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04271-4

  • Online ISBN: 978-3-030-04272-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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