A Data-driven Methodology towards Interpreting Readability against Software Properties

Karanikiotis, Thomas; Papamichail, Michail D.; Gonidelis, Ioannis; Karatza, Dimitra C.; Symeonidis, Andreas L.

doi:10.5220/0009891000610072

Cited by 3 publications

(1 citation statement)

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“…For example, comparing the readability of iterative versus recursive implementations of a factorial function can help determine which version is deemed more readable by a broader audience. Moreover, the data facilitate the design and development of readability models, either by using the source code directly [40,41] or the associated static analysis metrics [42][43][44]. When doing so, comparison with the state of the art is straightforward, as our dataset includes different metrics for readability [30][31][32][33].…”

Section: Discussionmentioning

confidence: 99%

Employing Source Code Quality Analytics for Enriching Code Snippets Data

Karanikiotis,

Diamantopoulos,

Symeonidis

2023

Data

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The availability of code snippets in online repositories like GitHub has led to an uptick in code reuse, this way further supporting an open-source component-based development paradigm. The likelihood of code reuse rises when the code components or snippets are of high quality, especially in terms of readability, making their integration and upkeep simpler. Toward this direction, we have developed a dataset of code snippets that takes into account both the functional and the quality characteristics of the snippets. The dataset is based on the CodeSearchNet corpus and comprises additional information, including static analysis metrics, code violations, readability assessments, and source code similarity metrics. Thus, using this dataset, both software researchers and practitioners can conveniently find and employ code snippets that satisfy diverse functional needs while also demonstrating excellent readability and maintainability.

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Section: Discussionmentioning

confidence: 99%

Employing Source Code Quality Analytics for Enriching Code Snippets Data

Karanikiotis,

Diamantopoulos,

Symeonidis

2023

Data

Self Cite

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Multilevel Readability Interpretation Against Software Properties: A Data-Centric Approach

Karanikiotis

Papamichail

Symeonidis

2021

Communications in Computer and Information Science

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A Notional Understanding of the Relationship between Code Readability and Software Complexity

et al. 2023

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Code readability and software complexity are considered essential components of software quality. They significantly impact software metrics, such as reusability and maintenance. The maintainability process consumes a high percentage of the software lifecycle cost, which is considered a very costly phase and should be given more focus and attention. For this reason, the importance of code readability and software complexity is addressed by considering the most time-consuming component in all software maintenance activities. This paper empirically studies the relationship between code readability and software complexity using various readability and complexity metrics and machine learning algorithms. The results are derived from an analysis dataset containing roughly 12,180 Java files, 25 readability features, and several complexity metric variables. Our study empirically shows how these two attributes affect each other. The code readability affects software complexity with 90.15% effectiveness using a decision tree classifier. In addition, the impact of software complexity on the readability of code using the decision tree classifier has a 90.01% prediction accuracy.

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A Data-driven Methodology towards Interpreting Readability against Software Properties

Cited by 3 publications

References 0 publications

Employing Source Code Quality Analytics for Enriching Code Snippets Data

Employing Source Code Quality Analytics for Enriching Code Snippets Data

Multilevel Readability Interpretation Against Software Properties: A Data-Centric Approach

A Notional Understanding of the Relationship between Code Readability and Software Complexity

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