The paper addresses the problem of identifying inconsistent method names in software development, which can lead to misunderstandings among developers and result in software defects . This issue is significant as inconsistent names do not accurately describe the functionality and semantics of their corresponding method bodies .

The paper presents a new, clean benchmark dataset that reflects real-world scenarios for this identification task, which is a novel contribution to the field . While previous studies have explored similar topics, this work is distinguished by its extensive empirical evaluation of state-of-the-art deep learning approaches under various settings, making it a fresh perspective on the problem .

The paper seeks to validate the hypothesis regarding the effectiveness of various deep learning (DL)-based approaches in identifying inconsistent method names within software code. It aims to evaluate these approaches on a newly constructed dataset that reflects real-world scenarios, thereby assessing their performance in both within-project and cross-project settings . The study also investigates the impact of the ratio of inconsistent to consistent method names on the performance of these approaches, as well as the influence of method body complexity on their success rates .

The paper titled "Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?" presents several new ideas, methods, and models aimed at improving the identification of inconsistent method names in software development. Below is a detailed analysis of the contributions made by the authors:

1. New Benchmark Construction

The authors introduce a new benchmark dataset, referred to as BenMark, which is large and reflective of real-world scenarios. This dataset includes 2,443 inconsistent methods and 1,296,743 consistent ones, constructed from 430 high-quality projects. The dataset is manually inspected to ensure its quality, addressing the limitations of previous datasets that may have led to false positives in inconsistency detection .

2. Empirical Study of DL-Based Approaches

The paper conducts an extensive empirical study on five state-of-the-art deep learning (DL)-based approaches for identifying inconsistent method names. These approaches include:

• CAN (Allamanis et al. 2016)
• IRMCC (Liu et al. 2019)
• MNIRE (Nguyen et al. 2020)
• Cognac (Wang et al. 2021a)
• GTNM (Liu et al. 2022)

The study evaluates these methods under different empirical settings, providing insights into their performance and limitations .

3. Evaluation of Performance

The authors highlight the need for a comprehensive evaluation of existing approaches in more natural datasets. They analyze where and why these approaches succeed or fail, which is crucial for both researchers and practitioners. The evaluation results suggest that the ratio of inconsistent to consistent methods is significantly smaller than previously thought, indicating a need for improved methods to handle imbalanced datasets .

4. Insights for Future Development

The paper provides key insights that serve as take-away messages for the future development of advanced approaches. It discusses the importance of understanding the performance of DL-based approaches in real-world scenarios and offers practical guidelines for improving these methods .

5. Methodological Contributions

The paper elaborates on the mainstream methods for identifying inconsistent method names, including information retrieval-based and generation-based approaches. It emphasizes the rationale behind generation-based approaches, which involve generating a method name for a specific method body and calculating lexical similarity to determine consistency .

6. Addressing Limitations of Previous Work

The authors critique existing datasets and methodologies, pointing out flaws in the construction methods that do not guarantee the association between method name changes and inconsistencies. This critique underscores the necessity for the new benchmark and the empirical study conducted in the paper .

In summary, the paper proposes a new benchmark dataset, conducts a thorough empirical evaluation of existing DL-based approaches, and provides insights and guidelines for future research in the identification of inconsistent method names. These contributions are significant for advancing the field and improving the accuracy of automated methods in software development. The paper "Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?" presents several characteristics and advantages of its proposed methods compared to previous approaches in the field of identifying inconsistent method names. Below is a detailed analysis based on the content of the paper:

1. Introduction of a New Benchmark Dataset

The paper introduces a new benchmark dataset, BenMark, which is large, clean, and manually inspected to reflect real-world scenarios. This dataset includes a significant number of inconsistent and consistent method names, addressing the limitations of previous datasets that may not have accurately represented the complexities of real-world software projects .

2. Comprehensive Evaluation of State-of-the-Art Approaches

The authors conduct an extensive empirical study on five state-of-the-art deep learning (DL)-based approaches, including both information retrieval (IR) and generation-based methods. This evaluation is performed on the newly constructed dataset, allowing for a more realistic assessment of the methods' performance in various application scenarios .

3. Insights into Performance Limitations

The paper provides insights into where and why existing approaches succeed or fail. For instance, it highlights that IR-based approaches perform better on methods with simple bodies and names that start with popular sub-tokens, while generation-based approaches struggle with ineffective similarity calculations and immature name generation techniques . This analysis is crucial for understanding the strengths and weaknesses of current methods and guiding future improvements.

4. Focus on Generation-Based Approaches

The paper emphasizes the rationale behind generation-based approaches, which involve generating a method name for a specific method body and calculating lexical similarity to determine consistency. This method contrasts with previous approaches that may not have effectively utilized contextual information, such as parameter types and return types, which are leveraged in the proposed methods .

5. Contrastive Learning for Improved Identification

The authors propose a more efficient method based on contrastive learning, which utilizes encoder networks to enhance the identification of inconsistent method names. This approach is designed to address the limitations of previous generation-based methods, particularly in handling narrow types of inconsistencies .

6. Addressing Imbalanced Datasets

The paper discusses the impact of the ratio of inconsistent to consistent method names on the performance of existing approaches. It reveals that many current methods are evaluated in datasets where the number of inconsistent names equals that of consistent ones, which is not reflective of real-world scenarios. The proposed methods aim to perform better in more realistic settings where consistent names are more prevalent .

7. Key Takeaways for Future Research

The paper concludes with key insights that serve as valuable takeaways for the future development of advanced approaches. These insights include the need for better similarity calculation metrics and improved method name generation techniques, which can significantly enhance the performance of inconsistency detection methods .

In summary, the paper's contributions include the introduction of a new benchmark dataset, a comprehensive evaluation of existing methods, insights into their performance limitations, and the proposal of more efficient methods based on contrastive learning. These characteristics and advantages position the proposed methods as significant advancements in the field of identifying inconsistent method names.

Related Researches and Noteworthy Researchers

Yes, there are several related researches in the field of identifying inconsistent method names. Noteworthy researchers include Taiming Wang, who has contributed significantly to the empirical study of deep learning-based approaches for this task . Other prominent researchers mentioned in the context are Allamanis et al., who proposed various methods for naming conventions and method name suggestions , and Liu et al., who have also conducted empirical studies in this area .

Key to the Solution

The key to the solution mentioned in the paper is the construction of a new benchmark dataset, referred to as BenMark, which includes a large-scale test dataset with 2,443 inconsistent methods and 1,296,743 consistent ones. This dataset aims to evaluate the performance of state-of-the-art deep learning approaches in real-world scenarios and to address the limitations of previous datasets that were either too balanced or flawed . The empirical study conducted on this dataset investigates the performance of selected deep learning approaches and aims to provide insights into their effectiveness in identifying inconsistent method names .

The experiments in the paper were designed using a comprehensive approach that included several key components:

1. Dataset Construction: The authors constructed a large test dataset called BenMark, which was clean and manually inspected to reflect real-world scenarios. This dataset was divided into 10 folds for 10-fold cross-validation experiments, where 10% of the projects were used for testing and 90% for training .

2. Training and Testing Data: The training data, referred to as CORPUS CP, was created from the projects of the other nine folds, while the testing data was constructed to ensure no overlap with the training data. This was done to avoid data leakage and to mimic real-world scenarios .

3. Balanced Datasets: To facilitate the evaluation of the approaches, two new testing datasets, BalancedData and NaturalData, were constructed. BalancedData contained an equal number of inconsistent and consistent method names, while NaturalData reused all the testing data from BenMark without controlling the ratio of method names .

4. Evaluation of Approaches: The paper evaluated five state-of-the-art deep learning-based approaches, including one information retrieval-based approach and four generation-based approaches, to identify inconsistent method names. The evaluation focused on understanding where and why these approaches succeed or fail .

Overall, the experimental design aimed to provide a thorough evaluation of the methods under different empirical settings, ensuring the results were robust and reflective of real-world challenges in identifying inconsistent method names .

The dataset used for quantitative evaluation is referred to as BenMark, which includes 2,443 inconsistent methods and 1,296,743 consistent ones, constructed from 430 high-quality projects . This dataset is designed to reflect real-world scenarios and is essential for evaluating the performance of deep learning-based approaches in identifying inconsistent method names .

Regarding the code, while the implementation of some evaluated approaches is publicly available, it is noted that the implementation of DeepName cannot run smoothly, and the authors did not provide feedback when contacted . Therefore, while some aspects may be open source, the overall availability and functionality of the code may vary.

The experiments and results presented in the paper "Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?" provide a substantial foundation for verifying scientific hypotheses related to the identification of inconsistent method names.

Comprehensive Evaluation
The paper conducts a comprehensive evaluation of five state-of-the-art deep learning (DL)-based approaches on a newly constructed dataset, BenMark, which includes a significant number of inconsistent and consistent methods. This dataset is noted for being clean and reflective of real-world scenarios, which enhances the validity of the findings .

Balanced Dataset
The authors emphasize the importance of a balanced dataset, as the ratio of inconsistent to consistent methods is often imbalanced in real-world scenarios. The construction of the dataset, which includes both consistent and inconsistent methods, allows for a more accurate assessment of the DL approaches' performance .

Empirical Study and Research Questions
The empirical study addresses critical research questions, such as the performance of DL approaches in both within-project and cross-project settings. This dual focus is essential for understanding the applicability of these methods in diverse scenarios, thereby supporting the hypotheses regarding their effectiveness .

Statistical Analysis
The paper employs statistical methods, such as the Wilcoxon-Mann-Whitney U-test, to analyze the complexity of method bodies and their impact on prediction accuracy. The results indicate significant differences in the lines of code (LOC) between successful and failed predictions, which supports the hypothesis that method complexity affects identification accuracy .

Conclusion
Overall, the experiments and results in the paper provide robust support for the scientific hypotheses being tested. The careful construction of the dataset, the comprehensive evaluation of various DL approaches, and the rigorous statistical analysis contribute to a strong foundation for further research in this area .

The paper makes several key contributions:

1. New Benchmark: It introduces a new, clean benchmark that has been thoroughly inspected manually. This benchmark is large and reflective of real-world scenarios for identifying inconsistent method names .

2. Empirical Study: The paper presents an extensive empirical study on representative deep learning (DL)-based approaches for the automated identification of inconsistent method names under various empirical settings .

3. Evaluation of Approaches: It evaluates five state-of-the-art DL-based approaches, including one information retrieval-based approach and four generation-based approaches, on a constructed large test dataset that is clean and closely resembles real-world scenarios .

These contributions aim to enhance the understanding and effectiveness of methods used to identify inconsistent method names in software development.

Future work can focus on several key areas to deepen the understanding and application of deep learning-based approaches for identifying inconsistent method names:

1. Exploration of Imbalanced Datasets: Investigating the performance of existing approaches on imbalanced datasets, as the current evaluations have primarily been conducted on balanced datasets. This could reveal how well these models perform in real-world scenarios where consistent method names vastly outnumber inconsistent ones .

2. Cross-Project Evaluation: Further empirical studies could be conducted to assess the effectiveness of deep learning models in cross-project settings, as initial findings suggest that switching from within-project to cross-project settings can yield different performance metrics . This could help in understanding the generalizability of these models across different codebases.

3. Benchmark Development: Continued development and refinement of benchmarks like BenMark, which includes a large-scale dataset of inconsistent and consistent method names, can provide a more accurate assessment of model performance. This includes reducing false positives and ensuring that the dataset reflects real-world coding practices .

4. Integration of Contextual Information: Future research could explore the integration of additional contextual information (e.g., parameter types, return types, and class names) to improve the accuracy of method name predictions. This could enhance the models' ability to generate and validate method names against their implementations .

5. Longitudinal Studies: Conducting longitudinal studies to track the performance of these models over time as coding practices evolve could provide insights into their adaptability and robustness in changing environments .

By addressing these areas, researchers can contribute to the advancement of automated methods for identifying inconsistent method names, ultimately improving software quality and maintainability.