Smart Formatter: Learning Coding Style from Existing Source Code

Corbo, F.; Grosso, C. Del; Penta, Massimiliano Di

doi:10.1109/icsm.2007.4362682

Cited by 13 publications

(2 citation statements)

References 4 publications

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“…Deißenböck and Pizka [6] provided guidelines for the production of highquality identifiers. Corbo et al proposed SMARTFORMAT-TER [19], a tool able to learn coding styles from an existing code base, and recommend new developers about indentation styles, usage of comments, and naming conventions used for different kinds of identifiers. Another approach for recommending coding styles was proposed by Reiss [20].…”

Section: Related Workmentioning

confidence: 99%

A New Family of Software Anti-patterns: Linguistic Anti-patterns

Arnaoudova

Penta

Antoniol

et al. 2013

2013 17th European Conference on Software Maintenance and Reengineering

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Abstract-Recent and past studies have shown that poor source code lexicon negatively affects software understandability, maintainability, and, overall, quality. Besides a poor usage of lexicon and documentation, sometimes a software artifact description is misleading with respect to its implementation. Consequently, developers will spend more time and effort when understanding these software artifacts, or even make wrong assumptions when they use them.This paper introduces the definition of software linguistic antipatterns, and defines a family of them, i.e., those related to inconsistencies (i) between method signatures, documentation, and behavior and (ii) between attribute names, types, and comments. Whereas "design" antipatterns represent recurring, poor design choices, linguistic antipatterns represent recurring, poor naming and commenting choices.The paper provides a first catalogue of one family of linguistic antipatterns, showing real examples of such antipatterns and explaining what kind of misunderstanding they can cause. Also, the paper proposes a dectector prototype for Java programs called LAPD (Linguistic Anti-Pattern Detector), and reports a study investigating the presence of linguistic antipatterns in four Java software projects.

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Section: Related Workmentioning

confidence: 99%

A New Family of Software Anti-patterns: Linguistic Anti-patterns

Arnaoudova

Penta

Antoniol

et al. 2013

2013 17th European Conference on Software Maintenance and Reengineering

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“…In software development, maintenance tasks occupy 80% of the software life cycle [2] and maintenance cost represents the true cost of software [3]. In performing the maintenance task, developers spend considerable time reading a program [4].…”

Section: Introductionmentioning

confidence: 99%

Method Verb Recommendation Using Association Rule Mining in a Set of Existing Projects

Kashiwabara

Ishio

Hata

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYIt is well-known that program readability is important for maintenance tasks. Method names are important identifiers for program readability because they are used for understanding the behavior of methods without reading a part of the program. Although developers can create a method name by arbitrarily choosing a verb and objects, the names are expected to represent the behavior consistently. However, it is not easy for developers to choose verbs and objects consistently since each developer may have a different notion of a suitable lexicon for method names. In this paper, we propose a technique to recommend candidate verbs for a method name so that developers can use various verbs consistently. We recommend candidate verbs likely to be used as a part of a method name, using association rules extracted from existing methods. To evaluate our technique, we have extracted rules from 445 open source projects written in Java and confirmed the accuracy of our approach by applying the extracted rules to several open source applications. As a result, we found that 84.9% of the considered methods in four projects are recommended the existing verb. Moreover, we found that 73.2% of the actual renamed methods in six projects are recommended the correct verb.

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Automated variable renaming: are we there yet?

et al. 2023

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Identifiers, such as method and variable names, form a large portion of source code. Therefore, low-quality identifiers can substantially hinder code comprehension. To support developers in using meaningful identifiers, several (semi-)automatic techniques have been proposed, mostly being data-driven (e.g., statistical language models, deep learning models) or relying on static code analysis. Still, limited empirical investigations have been performed on the effectiveness of such techniques for recommending developers with meaningful identifiers, possibly resulting in rename refactoring operations. We present a large-scale study investigating the potential of data-driven approaches to support automated variable renaming. We experiment with three state-of-the-art techniques: a statistical language model and two DL-based models. The three approaches have been trained and tested on three datasets we built with the goal of evaluating their ability to recommend meaningful variable identifiers. Our quantitative and qualitative analyses show the potential of such techniques that, under specific conditions, can provide valuable recommendations and are ready to be integrated in rename refactoring tools. Nonetheless, our results also highlight limitations of the experimented approaches that call for further research in this field.

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Smart Formatter: Learning Coding Style from Existing Source Code

Abstract: Abstract

Cited by 13 publications

References 4 publications

A New Family of Software Anti-patterns: Linguistic Anti-patterns

A New Family of Software Anti-patterns: Linguistic Anti-patterns

Method Verb Recommendation Using Association Rule Mining in a Set of Existing Projects

Automated variable renaming: are we there yet?

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