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A Machine Learning Approach for Source Code Similarity via Graph-Focused Features

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Machine Learning, Optimization, and Data Science (LOD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14505))

Abstract

Source code similarity aims at recognizing common characteristics between two different codes by means of their components. It plays a significant role in many activities regarding software development and analysis which have the potential of assisting software teams working on large codebases. Existing approaches aim at computing similarity between two codes by suitable representation of them which captures syntactic and semantic properties. However, they lack explainability and generalization for multiple languages comparison. Here, we present a preliminary result that attempts at providing a graph-focused representation of code by means of which clustering and classification of programs is possible while exposing explainability and generalizability characteristics.

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Notes

  1. 1.

    In C/C++, executable programs are obtained by linking together the code coming from a complete set of translation units. A translation unit is the portion of a program a compiler operates upon, and is constituted by a main file (typically with a .c/.C/.cxx/.cpp extension) along with all header files (typically with a .h/.H/.hpp extension) that the main file includes, directly or indirectly. A prerequisite of our approach is that the translation units to be analyzed are complete, so that the Clang compiler can process them without errors.

  2. 2.

    This phase is managed by using a custom LLVM-IR parser. The parser is generated using ANTLR [21] starting from the LLVM-IR 7.0.0 grammar.

  3. 3.

    In LLVM-IR, op-code refers to the instruction code (or name) that specifies the operation to be performed by the instruction.

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Acknowledgement

This project has been partially founded by the University of Parma (Italy), project number MUR_DM737_B_MAFI_BONNICI. V. Bonnici is partially supported by INdAM-GNCS, project number CUP_E55F22000270001, and by the CINI InfoLife laboratory.

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Authors and Affiliations

  1. Department of Mathematical, Physical and Computer Sciences, University of Parma, Parco Area delle Scienze, 53/A, 43124, Parma, Italy

    Giacomo Boldini, Alessio Diana, Vincenzo Arceri, Vincenzo Bonnici & Roberto Bagnara

Authors
  1. Giacomo Boldini
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  2. Alessio Diana
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  3. Vincenzo Arceri
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  4. Vincenzo Bonnici
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  5. Roberto Bagnara
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Corresponding author

Correspondence to Vincenzo Bonnici .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Catania, Italy

    Giuseppe Nicosia

  2. Newcastle University, Newcastle upon Tyne, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  6. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Boldini, G., Diana, A., Arceri, V., Bonnici, V., Bagnara, R. (2024). A Machine Learning Approach for Source Code Similarity via Graph-Focused Features. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham. https://doi.org/10.1007/978-3-031-53969-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-53969-5_5

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