Exploration of Convolutional Neural Network models for source code classification

Barchi, Francesco; Parisi, Emanuele; Urgese, Gianvito; Ficarra, Elisa; Acquaviva, Andrea

doi:10.1016/j.engappai.2020.104075

Cited by 24 publications

(31 citation statements)

References 9 publications

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“…Successively, [5], [6], [11], [12] improves accuracy results obtained by [4] exploiting deep learning models based on LSTM (Long Short-Term Memory). Even if promising, deep learning models do not allow insight into what static source code features are most significant for carrying out the classification task.…”

Section: B Machine Learning For Source Code Performance Estimationmentioning

confidence: 83%

Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

Parisi

Barchi

Bartolini

et al. 2021

2021 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of lowpower, parallel embedded architectures, this means finding the configuration, for instance in terms of the number of cores, leading to minimum energy consumption. Depending on the kernel to be executed, the energy optimal scaling configuration is not trivial. While recent work has focused on general-purpose systems to learn and predict the best execution target in terms of the execution time of a snippet of code or kernel (e.g. offload OpenCL kernel on multicore CPU or GPU), in this work we focus on static compile-time features to assess if they can be successfully used to predict the minimum energy configuration on PULP, an ultra-low-power architecture featuring an on-chip cluster of RISC-V processors. Experiments show that using machine learning models on the source code to select the best energy scaling configuration automatically is viable and has the potential to be used in the context of automatic system configuration for energy minimisation.

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Section: B Machine Learning For Source Code Performance Estimationmentioning

confidence: 83%

Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

Parisi

Barchi

Bartolini

et al. 2021

2021 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

Self Cite

View full text Add to dashboard Cite

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“…Activation functions enabling the training of DL model in a fast and accurate manner. There are many activation functions used in DL such as sigmoid, rectified linear unit (Relu) and hyperbolic tangent (Tanh) [24], [25]. Our model uses various functions such as the ReLU function as the activation function for the input and hidden layers, and the sigmoid function as the activation function for the output layer as shown in ( 2) and (3).…”

Section: Deep Convolutional Neural Networkmentioning

confidence: 99%

Deep convolutional neural network model for bad code smells detection based on oversampling method

Khleel

Nehéz²

2022

IJEECS

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Code <span>smells refers to any symptoms or anomalies in the source code that shows violation of design principles or implementation. Early detection of bad code smells improves software quality. Nowadays several artificial neural network (ANN) models have been used for different topics in software engineering: software defect prediction, software vulnerability detection, and code clone detection. It is not necessary to know the source of the data when using ANN models but require large training sets. Data imbalance is the main challenge of artificial intelligence techniques in detecting the code smells. To overcome these challenges, the objective of this study is to presents deep convolutional neural network (D-CNN) model with synthetic minority over-sampling technique (SMOTE) to detect bad code smells based on a set of Java projects. We considered four code-smell datasets which are God class, data class, feature envy and long method and the results were compared based on different performance measures. Experimental results show that the proposed model with oversampling techniques can provide better performance for code smells detection and prediction results can be further improved when the model is trained with more datasets. Moreover, more epochs and hidden layers help increase the accuracy of the model.</span>

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“…The dataset includes five different programming languages: C, C++, Java, Python, and JavaScript. We use NICAD 4 to filter out the duplicate code. Considering that each code file contains some salient information (the function name of the solution to each problem written in different programming language is the same, for example, the function name of codes to the problem of "finding the median of two positive-order arrays" are all called "findMe-dianSortedArrays" ), so we use "XXX" instead of all function names to each problem and the user-defined function names will not be replaced.…”

Section: Datasetsmentioning

confidence: 99%

“…This algorithm has achieved excellent results in the source code classification task. Barchi et al [4] explored the use of Convolutional Neural Networks (CNN) [24] to analyze program source code and proved that the CNN model can be successfully applied to source code classification. Compared with the most advanced methods, this method provided higher accuracy and less learning time.…”

Section: Related Work 61 Program Classificationmentioning

confidence: 99%

Unified Abstract Syntax Tree Representation Learning for Cross-Language Program Classification

Wang,

Yan,

Zhang

et al. 2022

Preprint

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Program classification can be regarded as a high-level abstraction of code, laying a foundation for various tasks related to source code comprehension, and has a very wide range of applications in the field of software engineering, such as code clone detection, code smell classification, defects classification, etc. The cross-language program classification can realize code transfer in different programming languages, and can also promote cross-language code reuse, thereby helping developers to write code quickly and reduce the development time of code transfer. Most of the existing studies focus on the semantic learning of the code, whilst few studies are devoted to cross-language tasks. The main challenge of crosslanguage program classification is how to extract semantic features of different programming languages. In order to cope with this difficulty, we propose a Unified Abstract Syntax Tree (namely UAST in this paper) neural network. In detail, the core idea of UAST consists of two unified mechanisms. First, UAST learns an AST representation by unifying the AST traversal sequence and graph-like AST structure for capturing semantic code features. Second, we construct a mechanism called unified vocabulary, which can reduce the feature gap between different programming languages, so it can achieve the role of cross-language program classification. Besides, we collect a dataset containing 20,000 files of five programming languages, which can be used as a benchmark dataset for the crosslanguage program classification task. We have done experiments on two datasets, and the results show that our proposed approach outperforms the state-of-the-art baselines in terms of four evaluation metrics (Precision, Recall, F1-score, and Accuracy).

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Exploration of Convolutional Neural Network models for source code classification

Cited by 24 publications

References 9 publications

Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

Deep convolutional neural network model for bad code smells detection based on oversampling method

Unified Abstract Syntax Tree Representation Learning for Cross-Language Program Classification

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