Learning a Metric for Code Readability

Buse, Raymond P.L.; Weimer, Westley

doi:10.1109/tse.2009.70

Cited by 289 publications

(256 citation statements)

References 23 publications

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“…A major contribution of Buse et al [12] was that their estimation result for code readability was compared and verified with actual perception scores voted by humans. Buse et al emphasized the importance of code readability [12].…”

Section: Measuring Code Readabilitymentioning

confidence: 99%

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Effect Analysis of Coding Convention Violations on Readability of Post-Delivered Code

Lee¹,

Lee²

2015

IEICE Trans. Inf. & Syst.

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SUMMARYAdherence to coding conventions during the code production stage of software development is essential. Benefits include enabling programmers to quickly understand the context of shared code, communicate with one another in a consistent manner, and easily maintain the source code at low costs. In reality, however, programmers tend to doubt or ignore the degree to which the quality of their code is affected by adherence to these guidelines. This paper addresses research questions such as "Do violations of coding conventions affect the readability of the produced code?", "What kinds of coding violations reduce code readability?", and "How much do variable factors such as developer experience, project size, team size, and project maturity influence coding violations?" To respond to these research questions, we explored 210 open-source Java projects with 117 coding conventions from the Sun standard checklist. We believe our findings and the analysis approach used in the paper will encourage programmers and QA managers to develop their own customized and effective coding style guidelines.

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Section: Measuring Code Readabilitymentioning

confidence: 99%

“…Other than studies by Buse et al [12] and Posnett et al [13], papers attempting to objectively estimate code readability are limited. A major contribution of Buse et al [12] was that their estimation result for code readability was compared and verified with actual perception scores voted by humans.…”

Section: Measuring Code Readabilitymentioning

confidence: 99%

Effect Analysis of Coding Convention Violations on Readability of Post-Delivered Code

Lee¹,

Lee²

2015

IEICE Trans. Inf. & Syst.

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“…This results in a longer line, negatively correlated with readability [6]. Moreover, the only identifier used for a local variable, l, is not meaningful.…”

Section: Discussionmentioning

confidence: 98%

“…Therefore, r = 0 means that the the classifier is confident in classifying it as unreadable, while r = 1 means that the classifier is sure that it is readable. The training set of the model is constituted by snippets (e.g., methods) manually annotated by human developers as for their readability; we used as trainig set the union of the datasets in the state of the art, i.e., the ones by Buse and Weimer [6], Dorn [9] and Scalabrino et al [21].…”

Section: Experiments Methodologymentioning

confidence: 99%

“…Recent studies defined approaches that automatically measure code readability [6,17,21]. They are based on structural [6,17], visual [9] and textual [21] features measured on source code. Given a dataset of snippets evaluated by developers, such approaches consist in training a classifier able to distinguish readable from unreadable ones.…”

Section: Background and Related Workmentioning

confidence: 99%

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An empirical investigation on the readability of manual and generated test cases

Grano

Scalabrino

Gall

et al. 2018

Proceedings of the 26th Conference on Program Comprehension

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Software testing is one of the most crucial tasks in the typical development process. Developers are usually required to write unit test cases for the code they implement. Since this is a time-consuming task, in last years many approaches and tools for automatic test case generation -such as EvoSuitehave been introduced. Nevertheless, developers have to maintain and evolve tests to sustain the changes in the source code; therefore, having readable test cases is important to ease such a process. However, it is still not clear whether developers make an effort in writing readable unit tests. Therefore, in this paper, we conduct an explorative study comparing the readability of manually written test cases with the classes they test. Moreover, we deepen such analysis looking at the readability of automatically generated test cases. Our results suggest that developers tend to neglect the readability of test cases and that automatically generated test cases are generally even less readable than manually written ones. ABSTRACTSoftware testing is one of the most crucial tasks in the typical development process. Developers are usually required to write unit test cases for the code they implement. Since this is a time-consuming task, in last years many approaches and tools for automatic test case generation -such as EvoSuite-have been introduced. Nevertheless, developers have to maintain and evolve tests to sustain the changes in the source code; therefore, having readable test cases is important to ease such a process. However, it is still not clear whether developers make an effort in writing readable unit tests. Therefore, in this paper, we conduct an explorative study comparing the readability of manually written test cases with the classes they test. Moreover, we deepen such analysis looking at the readability of automatically generated test cases. Our results suggest that developers tend to neglect the readability of test cases and that automatically generated test cases are generally even less readable than manually written ones. CCS CONCEPTS• Software and its engineering → Maintaining software; Search-based software engineering;

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Assessing code readability in Python programming courses using eye‐tracking

Segedinac,

Savić,

Zeljković

et al. 2023

Comp Applic In Engineering

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Code readability models are typically based on the code's structural and textual features, considering code readability as an objective category. However, readability is inherently subjective and dependent on the knowledge and experience of the reader analyzing the code. This paper assesses the readability of Python code statements commonly used in undergraduate programming courses. Our readability model is based on tracking the reader's eye movement during the while‐read phase. It uses machine learning (ML) techniques and relies on a novel set of features—observational features—that capture how the readers read the code. We experimented by tracking the eye movement of 90 undergraduate students while assessing the readability of 48 Python code snippets. We trained an ML model that predicts readability based on the collected observational data and the code snippet's structural and textual features. In our experiments, the XGBoost classifier trained using observational features exclusively achieved the best results (0.85 F‐measure). Using correlation analysis, we identified Python statements most affecting readability for undergraduate students and proposed implications for teaching Python programming. In line with findings for Java language, we found that constructs related to the code's size and complexity hurt the code's readability. Numerous comments also hindered readability, potentially due to their association with less readable code. Some Python‐specific statements (list comprehension, lambda function, and dictionary comprehension) harmed code readability, even though they were part of the curriculum. Tracking students' gaze indicated some additional factors, most notably nonlinearity introduced by if, for, while, try, and function call statements.

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Learning a Metric for Code Readability

Cited by 289 publications

References 23 publications

Effect Analysis of Coding Convention Violations on Readability of Post-Delivered Code

Effect Analysis of Coding Convention Violations on Readability of Post-Delivered Code

An empirical investigation on the readability of manual and generated test cases

Assessing code readability in Python programming courses using eye‐tracking

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