Deep mining of open source software bug repositories

Hamdy, Abeer; Ezzat, Gloria

doi:10.1080/1206212x.2020.1855705

Cited by 7 publications

(9 citation statements)

References 25 publications

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“…But, in the proposed CNN network, deep features are routed to the LSTM layer instead of the fully connected layer. The CNN network captures and interprets LSA‐based semantic vectors effectively, whereas the LSTM network identifies long‐short‐term dependencies 46 . To benefit from the advantages of both models, the proposed approach presents a combined CNN‐LSTM model for the automatic identification and classification of programming codes.…”

Section: Proposed Method: Cross‐language Oss Classificationmentioning

confidence: 99%

“…The CNN network captures and interprets LSA-based semantic vectors effectively, whereas the LSTM network identifies long-short-term dependencies. 46 To benefit from the advantages of both models, the proposed approach presents a combined CNN-LSTM model for the automatic identification and classification of programming codes. The proposed CNN-LSTM model is separated into two phases, as shown in Figure 6.…”

Section: Features Detection and Classification Using Cnn-lstmmentioning

confidence: 99%

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CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Ullah

Naeem

et al. 2022

Int J of Intelligent Sys

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Software similarity in different programming codes is a rapidly evolving field because of its numerous applications in software development, software cloning, software plagiarism, and software forensics. Currently, software researchers and developers search cross‐language open‐source repositories for similar applications for a variety of reasons, such as reusing programming code, analyzing different implementations, and looking for a better application. However, it is a challenging task because each programming language has a unique syntax and semantic structure. In this paper, a novel tool called Cross‐Language Software Similarity (CroLSSim) is designed to detect similar software applications written in different programming codes. First, the Abstract Syntax Tree (AST) features are collected from different programming codes. These are high‐quality features that can show the abstract view of each program. Then, Methods Description (MDrep) in combination with AST is used to examine the relationship among different method calls. Second, the Term Frequency Inverse Document Frequency approach is used to retrieve the local and global weights from AST‐MDrep features. Third, the Latent Semantic Analysis‐based features extraction and selection method is proposed to extract the semantic anchors in reduced dimensional space. Fourth, the Convolution Neural Network (CNN)‐based features extraction method is proposed to mine the deep features. Finally, a hybrid deep learning model of CNN‐Long‐Short‐Term Memory is designed to detect semantically similar software applications from these latent variables. The data set contains approximately 9.5K Java, 8.8K C#, and 7.4K C++ software applications obtained from GitHub. The proposed approach outperforms as compared with the state‐of‐the‐art methods.

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Section: Proposed Method: Cross‐language Oss Classificationmentioning

confidence: 99%

Section: Features Detection and Classification Using Cnn-lstmmentioning

confidence: 99%

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Ullah

Naeem

et al. 2022

Int J of Intelligent Sys

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show abstract

“…If needed, these can be used to gain additional insights into the software development process as a whole as well as into specific SDM activities. 64 As we focus on a single case, we use process mining to support exploring these different aspects related to the selected SDM elements.…”

Section: Phase 3-analysis Of Software Development Tools Logs For Sele...mentioning

confidence: 99%

“…Moreover, some software development tools logs contain additional information like priority of specific types of activities, pointers to related or preceding activities, and detailed unstructured descriptions of preformed activities and comments. If needed, these can be used to gain additional insights into the software development process as a whole as well as into specific SDM activities 64 . As we focus on a single case, we use process mining to support exploring these different aspects related to the selected SDM elements.…”

Section: Proposed Approachmentioning

confidence: 99%

Software Process Evaluation from User Perceptions and Log Data

Vavpotič

Bala

Mendling

et al. 2022

J Software Evolu Process

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Companies often claim to follow specific software development methodologies (SDM) when performing their software development process. These methodologies are often supported by dedicated tools that keep track of work activities carried out by developers. The purpose of this paper is to provide a novel approach that integrates analytical insights from both the perceptions of SDM stakeholders and software development tools logs to provide SDM improvement recommendations. This

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“…Many neural algorithms use different processing approaches or incorporate a range of techniques to provide a better system, which can result in a time-consuming and costly task that might hinder real-time inspection and analysis. The technique’s purpose is to identify automobiles within the provided traffic data [ 11 ]. The collection consists of numerous forms of roadside objects, including several types of cars and other things, such as roads and passengers.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

Wieczorek

Tahir

Akhter

et al. 2023

Sensors

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Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking of vehicles under varying extreme conditions has become one of the most challenging tasks. To deal with this, our proposed system presents an adaptive method for robustly recognizing several existing automobiles in dense traffic settings. Additionally, this research presents a broad framework for effective on-road vehicle recognition and detection. Furthermore, the proposed system focuses on challenges typically noticed in analyzing traffic scenes captured by in-vehicle cameras, such as consistent extraction of features. First, we performed frame conversion, background subtraction, and object shape optimization as preprocessing steps. Next, two important features (energy and deep optical flow) were extracted. The incorporation of energy and dense optical flow features in distance-adaptive window areas and subsequent processing over the fused features resulted in a greater capacity for discrimination. Next, a graph-mining-based approach was applied to select optimal features. Finally, the artificial neural network was adopted for detection and classification. The experimental results show significant performance in two benchmark datasets, including the LISA and KITTI 7 databases. The LISA dataset achieved a mean recognition rate of 93.75% on the LDB1 and LDB2 databases, whereas KITTI attained 82.85% accuracy on separate training of ANN.

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Deep mining of open source software bug repositories

Cited by 7 publications

References 25 publications

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Software Process Evaluation from User Perceptions and Log Data

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

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