Method name recommendation based on source code metrics

Parsa, Saeed; Zakeri‐Nasrabadi, Morteza; Ekhtiarzadeh, Masoud; Ramezani, Mohammad

doi:10.1016/j.cola.2022.101177

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…Parsa and colleagues [13] pointed out that Code2vec cannot capture semantic information in method names, and Code2seq cannot handle long method names. Their method, proposed in the paper, addresses these issues by using source code metrics and naming patterns, thus improving the accuracy of method name recommendation.…”

Section: Context-based Methodsmentioning

confidence: 99%

Review of method name recommendation

Zhang,

Luo

2024

ACE

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In software engineering projects, method name recommendation is a critical task aimed at assisting developers in selecting appropriate method names to enhance code readability and maintainability. Given the significance of method name recommendation, numerous researchers have delved into improving the quality of method names. In this paper, we systematically review the existing method name recommendation techniques and conduct a comprehensive analysis of their principles, characteristics, and performance. Furthermore, we summarize the comparisons and evaluations among various methods to aid researchers in choosing method name recommendation techniques that suit their specific requirements. Lastly, we highlight the future research directions in this field. Through this review, readers will gain a clearer understanding of the current state of method name recommendation, providing valuable guidance for further research and development.

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Section: Context-based Methodsmentioning

confidence: 99%

Review of method name recommendation

Zhang,

Luo

2024

ACE

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“…In [9,34], the method name prediction problem was considered, where the problem was reduced to the algorithm classification problem. Similar to [33], the approach described in [34] was based on code metrics, but used a KNN model, determining the k methods that were most similar to the given method with a known name.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Program Representations Based on Abstract Syntax Trees and Higher-Order Markov Chains for Source Code Classification Task

Gorchakov,

Demidova,

Sovietov

2023

Future Internet

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In this paper we consider the research and development of classifiers that are trained to predict the task solved by source code. Possible applications of such task detection algorithms include method name prediction, hardware–software partitioning, programming standard violation detection, and semantic code duplication search. We provide the comparative analysis of modern approaches to source code transformation into vector-based representations that extend the variety of classification and clustering algorithms that can be used for intelligent source code analysis. These approaches include word2vec, code2vec, first-order and second-order Markov chains constructed from abstract syntax trees (AST), histograms of assembly language instruction opcodes, and histograms of AST node types. The vectors obtained with the forementioned approaches are then used to train such classification algorithms as k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP). The obtained results show that the use of program vectors based on first-order AST-based Markov chains with an RF-based classifier leads to the highest accuracy, precision, recall, and F1 score. Increasing the order of Markov chains considerably increases the dimensionality of a vector, without any improvements in classifier quality, so we assume that first-order Markov chains are best suitable for real world applications. Additionally, the experimental study shows that first-order AST-based Markov chains are least sensitive to the used classification algorithm.

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“… The first type is Physical Lines of Code (LOC), which deals with all the source code lines of the software without any consideration for its content. This scale calculates only the software that will be delivered to the customer and is sometimes referred to as Non-Comment Lines of Code (NCLOC) or effective Lines of Code (eLOC), excluding blank spaces and comments (Parsa et al, 2023).…”

Section: Wwwminarjournalcommentioning

confidence: 99%

Measuring Maintainability Index Before and After Code Refactoring

I. KHALEEL,

Khaleel AL-KHATOUNI

2023

MINAR Journal

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Measuring the maintainability index of software is crucial to ensure bettermaintenance and improve quality. Refactoring code is important in improving software quality and increasing maintainability. However,understanding the relationship between refactoring code and the maintainability index is crucial for software developers and maintenance engineers. An assistive software tool called MMIBAR was developed, which provides a set of metrics for calculating the maintainability index of the source code before and after refactoring code that contains cloned programming code. This research contributes to the understanding of the relationship between refactoring code and the maintainability index, and proposes a software tool that can help software developers and maintenance engineers improve software quality and increase maintainability. The research involved the development of the MMIBAR software tool and the application of its metrics to measure the maintainability index of the source code before and after refactoring code that contains cloned programming code. The results of the study demonstrate that refactoring code can significantly improve software quality and increase maintainability. The MMIBAR software tool provides a useful set of metrics for measuring the maintainability index of source code, and can be used to identify areas of code that need further refactoring. The research shows that refactoring code is crucial in improving software quality and increasing maintainability. The MMIBAR software tool can help software developers and maintenance engineers identify areas of code that need further refactoring, and ultimately improve the overall quality of software systems

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Method name recommendation based on source code metrics

Cited by 5 publications

References 12 publications

Review of method name recommendation

Review of method name recommendation

Analysis of Program Representations Based on Abstract Syntax Trees and Higher-Order Markov Chains for Source Code Classification Task

Measuring Maintainability Index Before and After Code Refactoring

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