A Novel Approach for Code Smells Detection Based on Deep Learning

Lin, Tao; Fu, Xue; Fu, Chen; Li, Luqun

doi:10.1007/978-3-030-80851-8_12

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…There are several choices in the two attempts to work on providing the location of duplicate error reports [8]. The first challenge is very fundamental in that separating the most efficient features will help in the best copy discovery; For instance, it cannot likely to distinguish oranges and applies based on their length [9]. Similarly, the volume is louder when it comes to length, width and height.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Deep Learning Model for Software Bug Detection and Classification

Sivapurnima¹,

Manjula²

2023

Computer Systems Science and Engineering

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Software is unavoidable in software development and maintenance. In literature, many methods are discussed which fails to achieve efficient software bug detection and classification. In this paper, efficient Adaptive Deep Learning Model (ADLM) is developed for automatic duplicate bug report detection and classification process. The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory (CRF-LSTM) and Dingo Optimizer (DO). In the CRF, the DO can be consumed to choose the efficient weight value in network. The proposed automatic bug report detection is proceeding with three stages like pre-processing, feature extraction in addition bug detection with classification. Initially, the bug report input dataset is gathered from the online source system. In the pre-processing phase, the unwanted information from the input data are removed by using cleaning text, convert data types and null value replacement. The pre-processed data is sent into the feature extraction phase. In the feature extraction phase, the four types of feature extraction method are utilized such as contextual, categorical, temporal and textual. Finally, the features are sent to the proposed ADLM for automatic duplication bug report detection and classification. The proposed methodology is proceeding with two phases such as training and testing phases. Based on the working process, the bugs are detected and classified from the input data. The projected technique is assessed by analyzing performance metrics such as accuracy, precision, Recall, F_Measure and kappa.

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Section: Introductionmentioning

confidence: 99%

Adaptive Deep Learning Model for Software Bug Detection and Classification

Sivapurnima¹,

Manjula²

2023

Computer Systems Science and Engineering

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An empirical study into the effects of transpilation on quantum circuit smells

Stefano,

Nucci,

Palomba

et al. 2024

Empir Software Eng

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Quantum computing is a promising field that can solve complex problems beyond traditional computers’ capabilities. Developing high-quality quantum software applications, called quantum software engineering, has recently gained attention. However, quantum software development faces challenges related to code quality. A recent study found that many open-source quantum programs are affected by quantum-specific code smells, with long circuit being the most common. While the study provided relevant insights into the prevalence of code smells in quantum circuits, it did not explore the potential effect of transpilation, a necessary step for executing quantum computer programs, on the emergence of code smells. Indeed, transpilation might alter those characteristics employed to detect the presence of a smell on a circuit. To address this limitation, we present a new study investigating the impact of transpilation on quantum-specific code smells and how different target gate sets affect the results. We conducted experiments on 17 open-source quantum programs alongside a set of 100 synthetic circuits. We found that transpilation can significantly alter the metrics that are used to detect code smells, even into previously smell-free circuits, with the long circuit smell being the most susceptible to transpilation. Furthermore, the choice of the gate set significantly influences the presence and severity of code smells in transpiled circuits, highlighting the need for careful gate set selection to mitigate their impact. These findings have implications for circuit optimization and high-quality quantum software development. Further research is needed to understand the consequences of code smells and their potential impact on quantum computations, considering the characteristics and constraints of different gate sets and hardware platforms.

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