On recommending meaningful names in source and UML

Kuhn, Adrian

doi:10.1145/1808920.1808932

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…For instance, [23] analyzes designer actions in the GUI of a model editor to detect ongoing high-level activities from a predefined catalog (e.g., a refactoring) and propose actions to auto-complete the activity. [21] suggests meaningful names for methods and UML model elements. Meanwhile, [35] suggests completions of a domain-specific model in order to satisfy well-formedness rules.…”

Section: Related Workmentioning

confidence: 99%

An NLP-Based Architecture for the Autocompletion of Partial Domain Models

Burgueño

Clarisó

Gérard

et al. 2021

Lecture Notes in Computer Science

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Domain models capture the key concepts and relationships of a business domain. Typically, domain models are manually defined by software designers in the initial phases of a software development cycle, based on their interactions with the client and their own domain expertise. Given the key role of domain models in the quality of the final system, it is important that they properly reflect the reality of the business. To facilitate the definition of domain models and improve their quality, we propose to move towards a more assisted domain modeling building process where an NLP-based assistant will provide autocomplete suggestions for the partial model under construction based on the automatic analysis of the textual information available for the project (contextual knowledge) and/or its related business domain (general knowledge). The process will also take into account the feedback collected from the designer's interaction with the assistant. We have developed a proof-of-concept tool and have performed a preliminary evaluation that shows promising results.

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Section: Related Workmentioning

confidence: 99%

An NLP-Based Architecture for the Autocompletion of Partial Domain Models

Burgueño

Clarisó

Gérard

et al. 2021

Lecture Notes in Computer Science

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“…Abebe et al proposed an approach to suggest identifiers on completion by constructing ontology tree [1]. Kuhn investigated likelihood of words in software component under binomial distribution and suggested an idea to utilize it for recommendation of names in source code and UML [6,7]. Thies and Roth proposed an approach to recommend method names as refactoring by analysing assignment relationship between variables [15].…”

Section: Introductionmentioning

confidence: 99%

An approach for evaluating and suggesting method names using n-gram models

Suzuki

Sakamoto

Ishikawa

et al. 2014

Proceedings of the 22nd International Conference on Program Comprehension

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Method names are important for the software development process. It has been shown by some studies that the quality of method names affects software comprehension. In response, some approaches that evaluate comprehensibility of method names have been proposed. However, the effectiveness of existing approaches is limited because they focus on part of names.To deal with the limitation, we propose a novel approach for evaluating comprehensibility of method names and suggesting comprehensible method names using n-gram models. We implemented a prototype tool and conducted two experiments as a case study. Our experiments show that our approach can correctly evaluate 75% method names and successfully suggest 92% actual third words of method names.

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