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Improving code readability classification using convolutional neural networks

INFORMATION AND SOFTWARE TECHNOLOGY 2018年 104卷 60-71页

作者： Mi, Qing Keung, Jacky Xiao, Yan Mensah, Solomon Gao, Yujin City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

Context code readability classification (which refers to classification of a piece of source code as either readable or unreadable) has attracted increasing concern in academia and industry. To construct accurate classification models, previous studies depended mainly upon handcrafted features. However, the manual feature engineering process is usually labor-intensive and can capture only partial information about the source code, which is likely to limit the model performance. Objective: To improve code readability classification, we propose the use of Convolutional Neural Networks (ConvNets). Method: We first introduce a representation strategy (with different granularities) to transform source codes into integer matrices as the input to ConvNets. We then propose DeepCRM, a deep learning-based model for code readability classification. DeepCRM consists of three separate ConvNets with identical architectures that are trained on data preprocessed in different ways. We evaluate our approach against five state-of-the-art code readability models. Results: The experimental results show that DeepCRM can outperform previous approaches. The improvement in accuracy ranges from 2.4% to 17.2%. Conclusions: By eliminating the need for manual feature engineering, DeepCRM provides a relatively improved performance, confirming the efficacy of deep learning techniques in the task of code readability classification.

关键词： code readability Convolutional Neural Network Deep learning Program comprehension Empirical software engineering Open source software

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How does code readability change during software evolution?

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EMPIRICAL SOFTWARE ENGINEERING 2020年第6期25卷 5374-5412页

作者： Piantadosi, Valentina Fierro, Fabiana Scalabrino, Simone Serebrenik, Alexander Oliveto, Rocco Univ Molise Pesche IS Italy Eindhoven Univ Technol Eindhoven Netherlands

code reading is one of the most frequent activities in software maintenance. Such an activity aims at acquiring information from the code and, thus, it is a prerequisite for program comprehension: developers need to read the source code they are going to modify before implementing changes. As the code changes, so does its readability;however, it is not clear yethowcode readability changes during software evolution. To understand how code readability changes when software evolves, we studied the history of 25 open source systems. We modeled code readability evolution by defining four states in which a file can be at a certain point of time (non-existing,other-name,readable, andunreadable). We used the data gathered to infer the probability of transitioning from one state to another one. In addition, we also manually checked a significant sample of transitions to compute the performance of the state-of-the-art readability prediction model we used to calculate the transition probabilities. With this manual analysis, we found that the tool correctly classifies all the transitions in the majority of the cases, even if there is a loss of accuracy compared to the single-version readability estimation. Our results show that most of the source code files are created readable. Moreover, we observed that only a minority of the commits change the readability state. Finally, we manually carried out qualitative analysis to understand what makes code unreadable and what developers do to prevent this. Using our results we propose some guidelines (i) to reduce the risk of code readability erosion and (ii) to promote best practices that make code readable.

关键词： code readability Software evolution Mining software repositories

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How do Developers Improve code readability? An Empirical Study of Pull Requests 39

How do Developers Improve Code Readability? An Empirical Stu...

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39th IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： Dantas, Carlos Eduardo C. Rocha, Adriano M. Maia, Marcelo A. Univ Fed Uberlandia Uberlandia MG Brazil

ISBN: (纸本)9798350327830

readability models and tools have been proposed to measure the effort to read code. However, these models are not completely able to capture the quality improvements in code as perceived by developers. To investigate possible features for new readability models and production-ready tools, we aim to better understand the types of readability improvements performed by developers when actually improving code readability, and identify discrepancies between suggestions of automatic static tools and the actual improvements performed by developers. We collected 370 code readability improvements from 284 Merged Pull Requests (PRs) under 109 GitHub repositories and produce a catalog with 26 different types of code readability improvements, where in most of the scenarios, the developers improved the code readability to be more intuitive, modular, and less verbose. Surprisingly, SonarQube only detected 26 out of the 370 code readability improvements. This suggests that some of the catalog produced has not yet been addressed by SonarQube rules, highlighting the potential for improvement in Automatic static analysis tools (ASAT) code readability rules as they are perceived by developers.

关键词： code readability pull request code review automatic static analysis tools sonarqube

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Automated code readability Feedback on Student Awareness 21st

Automated Code Readability Feedback on Student Awareness

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21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Karnalim, Oscar Sujadi, Sendy Ferdian Nathasya, Rossevine Artha Univ Kristen Maranatha Suria Sumantri St 65 Bandung 40164 Indonesia

ISBN: (纸本)9783031618901;9783031618918

Despite its importance in industry, writing readable code is often overlooked in academia. We developed CQIS, an automated checker that automatically provides feedback about code readability. Each time a program is submitted, a code readability report will be generated, and its unique link will be sent to the author. The report highlights parts of the submitted program that can be made clearer. The use of the checker needs to be evaluated, whether students are aware about code readability due to the checker. There is also a need to know which code readability aspects that students are least aware of and need further attention. Consequently, we evaluate the checker for one academic year (six courses with 222 students), and directly ask students about their awareness of code readability. Students seem to be generally more aware of code readability aspects, especially about having clear comments and having only useful variables. Students occasionally check the quality of their code after the code works at functional level. Few misconceptions might occur on higher programming level.

关键词： code readability Immediate Feedback Programming Engineering Education

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Using Eye Tracking Technology to Analyze the Impact of Stylistic Inconsistency on code readability

Using Eye Tracking Technology to Analyze the Impact of Styli...

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IEEE International Conference on Software Quality, Reliability and Security (Companion Volume)

作者： Mi, Qing Keung, Jacky Huang, Jianglin Xiao, Yan City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781538620724

A number of research efforts have focused in the area of programming style. However, to the best of our knowledge, there is little sound and solid evidence of how and to what extent can stylistic inconsistency impact the readability and maintainability of the source code. To bridge the research gap, we design an empirical experiment in which eye tracking technology is introduced to quantitatively reflect developers' cognitive efforts and mental processes when encountering the inconsistency issue.

关键词： programming style stylistic inconsistency eye tracking technology code readability program comprehension

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Using Deep Learning to Automatically Improve code readability 38

Using Deep Learning to Automatically Improve Code Readabilit...

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38th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Vitale, Antonio Piantadosi, Valentina Scalabrino, Simone Oliveto, Rocco Univ Molise STAKE Lab Campobasso Italy

ISBN: (纸本)9798350329964

Reading source code occupies most of developer's daily activities. Any maintenance and evolution task requires developers to read and understand the code they are going to modify. For this reason, previous research focused on the definition of techniques to automatically assess the readability of a given snippet. However, when many unreadable code sections are detected, developers might be required to manually modify them all to improve their readability. While existing approaches aim at solving specific readability-related issues, such as improving variable names or fixing styling issues, there is still no approach to automatically suggest which actions should be taken to improve code readability. In this paper, we define the first holistic readability-improving approach. As a first contribution, we introduce a methodology for automatically identifying readability-improving commits, and we use it to build a large dataset of 122k commits by mining the whole revision history of all the projects hosted on GitHub between 2015 and 2022. We show that such a methodology has similar to 86% accuracy. As a second contribution, we train and test the T5 model to emulate what developers did to improve readability. We show that our model achieves a perfect prediction accuracy between 21% and 28%. The results of a manual evaluation we performed on 500 predictions shows that when the model does not change the behavior of the input and it applies changes (34% of the cases), in the large majority of the cases (79.4%) it allows to improve code readability.

关键词： code readability large language models t5

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Measurement of Source code readability Using Word Concreteness and Memory Retention of Variable Names 41

Measurement of Source Code Readability Using Word Concretene...

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41st IEEE Annual Computer Software and Applications Conference (COMPSAC)

作者： Xu, Weifeng Xu, Dianxiang Deng, Lin Bowie State Univ Dept Comp Sci Bowie MD 20715 USA Towosn Univ Dept Comp & Info Sci Towosn MD USA

ISBN: (纸本)9781538603673

Source code readability is critical to software quality assurance and maintenance. In this paper, we present a novel approach to the automated measurement of source code readability based on Word Concreteness and Memory Retention (WCMR) of variable names. The approach considers programming and maintenance as processes of organizing variables and their operations to describe solutions to specific problems. The overall readability of given source code is calculated from the readability of all variables contained in the source code. The readability of each variable is determined by how easily its meaning is memorized (i.e., word concreteness) and how quickly they are forgotten over time (i.e., memory retention). Our empirical study has used 14 open source applications with over a half-million lines of code and 10,000 warning defects. The result shows that the WCMR-based source code readability negatively correlates strongly with overall warning defect rates, and particularly with such warning as bad programming practices, code vulnerability, and correctness bug warning.

关键词： code readability Variable Definitions and References Word Concreteness Memory Retention

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Linking code readability, Structure, and Comprehension among Novices: It's Complicated 41

Linking Code Readability, Structure, and Comprehension among...

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41st IEEE/ACM International Conference on Software Engineering - Software Engineering Education and Training (ICSE-SEET)

作者： Wiese, Eliane S. Rafferty, Anna N. Fox, Armando Univ Utah Salt Lake City UT 84112 USA Carleton Coll Northfield MN 55057 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781728110004

Novices' functionally-correct code is often redundant, verbose, or un-idiomatic. Such code could indicate shallow understanding of the programming language, or unfamiliarity with experts' preferences for code structure. Understanding why novices write poorly is important for designing instruction and tools to help novices write elegantly. 231 novices judged style and readability for sets of code snippets targeting seven topics. Within each set, functionality was the same, but the writing followed either common novice patterns or a more elegant, "expert" pattern. Overall, 76% of novices thought the "expert" snippets had the best style, but only 64% said those snippets were most readable. However, comprehension was similar for both "expert" and novice patterns, regardless of readability preferences. This suggests that students who prefer novice patterns do not necessarily have deep misunderstandings about the programming language. One topic included a code-writing task, and students' readability preferences were predictive of their code-writing patterns, suggesting that readability preferences reflect writing choices rather than comprehension. Thus, novices may benefit from lightweight tools that identify common patterns and suggest an "expert" solution, while helping them see that the "expert" solution is more readable than they think.

关键词： computer science education code readability novice code comprehension

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A Gamification Technique for Motivating Students to Learn code readability in Software Engineering

A Gamification Technique for Motivating Students to Learn Co...

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International Symposium on Educational Technology (ISET)

作者： Mi, Qing Keung, Jacky Mei, Xiupei Xiao, Yan Chan, W. K. City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781538672099

code readability is one of the important software quality attributes that computer science students need to learn in their programming classes, unfortunately most of the students do not have the necessary work experience or background to appreciate the importance of code readability. Traditional methods of learning code readability tend to be less than interactive and practical in the classroom environment. With the advent of gamification technique, this study introduced a new interactive teaching method and implemented as GamiCRS, an online platform for students to learn code readability. The focus was on incorporating game-based mechanisms to enable students with positive attitudes towards a more interesting learning process. A complete incentive and reward model is proposed in the study together with a combination of both intrinsic and extrinsic motivators identified. To ensure its dynamic efficacy, a field experiment was carried out to compare GamiCRS with its non-gamified counterparts and to evaluate learning outcomes. The empirical results show a positive effect towards the application of GamiCRS in the classroom environment. As many learning activities in software engineering are typically challenging and seldom amusing, gamification can thus be applied as a compelling addition to supporting a wider variety of teaching tactics.

关键词： code readability Gamification Education Crowdsourcing Motivation Technology Acceptance Model

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Reassessing Java code readability Models with a Human-Centered Approach 32

Reassessing Java Code Readability Models with a Human-Center...

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32nd IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Sergeyuk, Agnia Lvova, Olga Titov, Sergey Serova, Anastasiia Bagirov, Farid Kirillova, Evgeniia Bryksin, Timofey JetBrains Res Belgrade Serbia JetBrains Yerevan Armenia JetBrains Paphos Cyprus JetBrains Res Paphos Cyprus JetBrains Res Munich Germany JetBrains Res Limassol Cyprus

ISBN: (纸本)9798400705861

To ensure that Large Language Models (LLMs) effectively support user productivity, they need to be adjusted. Existing code readability (CR) models can guide this alignment. However, there are concerns about their relevance in modern software engineering since they often miss the developers' notion of readability and rely on outdated code. This research assesses existing Java CR models for LLM adjustments, measuring the correlation between their and developers' evaluations of AI-generated Java code. Using the Repertory Grid Technique with 15 developers, we identified 12 key code aspects influencing CR that were consequently assessed by 390 programmers when labeling 120 AI-generated snippets. Our findings indicate that when AI generates concise and executable code, it's often considered readable by CR models and developers. However, a limited correlation between these evaluations underscores the importance of future research on learning objectives for adjusting LLMs and on the aspects influencing CR evaluations included in predictive models.

关键词： code readability code readability Models Repertory Grid Technique AI-Generated code Human-Computer Interaction

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