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Towards automatically generating block comments for code snippets

INFORMATION AND SOFTWARE TECHNOLOGY 2020年 127卷 106373-106373页

作者： Huang, Yuan Huang, Shaohao Chen, Huanchao Chen, Xiangping Zheng, Zibin Luo, Xiapu Jia, Nan Hu, Xinyu Zhou, Xiaocong Sun Yat Sen Univ Natl Engn Res Ctr Digital Life Sch Data & Comp Sci Guangzhou Peoples R China Sun Yat Sen Univ Sch Commun & Design Guangdong Key Lab Big Data Anal & Simulat Publ Op Guangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Hebei GEO Univ Sch Management Sci & Engn Shijiazhuang Hebei Peoples R China

code commenting is a common programming practice of practical importance to help developers review and comprehend source code. There are two main types of code comments for a method: header comments that summarize the method functionality located before a method, and block comments that describe the functionality of the code snippets within a method. Inspired by the effectiveness of deep learning techniques in the NLP field, many studies focus on using the machine translation model to automatically generate comment for the source code. Because the data set of block comments is difficult to collect, current studies focus more on the automatic generation of header comments than that of block comments. However, block comments are important for program comprehension due to their explanation role for the code snippets in a method. To fill the gap, we have proposed an approach that combines heuristic rules and learning-based method to collect a large number of comment-code pairs from 1,032 open source projects in our previous study. In this paper, we propose a reinforcement learning-based method, RL-BlockCom, to automatically generate block comments for code snippets based on the collected comment-code pairs. Specifically, we utilize the abstract syntax tree (i.e., AST) of a code snippet to generate a token sequence with a statement-based traversal way. Then we propose a composite learning model, which combines the actor-critic algorithm of reinforcement learning with the encoder-decoder algorithm, to generate block comments. On the data set of the comment-code pairs, the BLEU-4 score of our method is 24.28, which outperforms the baselines and state-of-the-art in comment generation.

关键词： Automatic comment generation source code summarization code comment scope Reinforcement learning

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Fret: Functional Reinforced Transformer With BERT for code summarization

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IEEE ACCESS 2020年 8卷 135591-135604页

作者： Wang, Ruyun Zhang, Hanwen Lu, Guoliang Lyu, Lei Lyu, Chen Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Shandong Univ Sch Mech Engn Jinan 250061 Shandong Peoples R China

code summarization has long been viewed as a challenge in software engineering because of the difficulties of understanding source code and generating natural language. Some mainstream methods combine abstract syntax trees with language models to capture the structural information of the source code and generate relatively satisfactory comments. However, these methods are still deficient in code understanding and limited by the long dependency problem. In this paper, we propose a novel model called Fret, which stands for Functional REinforced Transformer with BERT. The model provides a new way to generate code comments by learning code functionalities and deepening code understanding while alleviating the problem of long dependency. For this purpose, a novel reinforcer is proposed for learning the functional contents of code so that more accurate summaries to describe the code functionalities can be generated. In addition, a more efficient algorithm is newly designed to capture the source code structure. The experimental results show that the effectiveness of our model is remarkable. Fret significantly outperforms all the state-of-the-art methods we examine. It pushes the BLEU-4 score to 24.32 for Java code summarization (14.23% absolute improvement) and the ROUGE-L score to 40.12 for Python. An ablation test is also conducted to further explore the impact of each component of our method.

关键词： Bit error rate Neural networks Task analysis Semantics Software engineering Natural languages Syntactics BERT language representation model software engineering source code summarization transformer network

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Generating summaries for methods of event-driven programs: An Android case study

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JOURNAL OF SYSTEMS AND SOFTWARE 2020年 170卷 110800-110800页

作者： Aghamohammadi, Alireza Izadi, Maliheh Heydarnoori, Abbas Sharif Univ Technol Dept Comp Engn Tehran Iran

The lack of proper documentation makes program comprehension a cumbersome process for developers. source code summarization is one of the existing solutions to this problem. Many approaches have been proposed to summarize source code in recent years. A prevalent weakness of these solutions is that they do not pay much attention to interactions among elements of software. An element is simply a callable code snippet such as a method or even a clickable button. As a result, these approaches cannot be applied to event-driven programs, such as Android applications, because they have specific features such as numerous interactions between their elements. To tackle this problem, we propose a novel approach based on deep neural networks and dynamic call graphs to generate summaries for methods of event-driven programs. First, we collect a set of comment/code pairs from Github and train a deep neural network on the set. Afterward, by exploiting a dynamic call graph, the Pagerank algorithm, and the pre-trained deep neural network, we generate summaries. An empirical evaluation with 14 real-world Android applications and 42 participants indicates 32.3% BLEU4 which is a definite improvement compared to the existing state-of-the-art techniques. We also assessed the informativeness and naturalness of our generated summaries from developers' perspectives and showed they are sufficiently understandable and informative. (C) 2020 Elsevier Inc. All rights reserved.

关键词： source code summarization Neural machine translation Event-driven programs Deep learning

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Developer Reading Behavior While Summarizing Java Methods: Size and Context Matters 19

Developer Reading Behavior While Summarizing Java Methods: S...

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41st IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Abid, Nahla J. Sharif, Bonita Dragan, Natalia Alrasheed, Hend Maletic, Jonathan I. Taibah Univ Dept Comp Sci Madinah Saudi Arabia Univ Nebraska Lincoln Dept Comp Sci & Engn Lincoln NE 68588 USA King Saud Univ Dept Informat Technol Riyadh Saudi Arabia Kent State Univ Dept Comp Sci Kent OH 44242 USA

ISBN: (纸本)9781728108698

An eye-tracking study of 18 developers reading and summarizing Java methods is presented. The developers provide a written summary for methods assigned to them. In total, 63 methods are used from five different systems. Previous studies on this topic use only short methods presented in isolation usually as images. In contrast, this work presents the study in the Eclipse IDE allowing access to all the source code in the system. The developer can navigate via scrolling and switching files while writing the summary. New eye-tracking infrastructure allows for this improvement in the study environment. Data collected includes eye gazes on source code, written summaries, and time to complete each summary. Unlike prior work that concluded developers focus on the signature the most, these results indicate that they tend to focus on the method body more than the signature. Moreover, both experts and novices tend to revisit control flow terms rather than reading them for a long period. They also spend a significant amount of gaze time and have higher gaze visits when they read call terms. Experts tend to revisit the body of the method significantly more frequently than its signature as the size of the method increases. Moreover, experts tend to write their summaries from source code lines that they read the most.

关键词： source code summarization eye tracking program comprehension empirical study

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A Neural Model for Generating Natural Language Summaries of Program Subroutines 19

A Neural Model for Generating Natural Language Summaries of ...

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41st IEEE/ACM International Conference on Software Engineering (ICSE)

作者： LeClair, Alexander Jiang, Siyuan McMillan, Collin Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA Eastern Michigan Univ Dept Comp Sci Ypsilanti MI 48197 USA

ISBN: (纸本)9781728108698

source code summarization - creating natural language descriptions of source code behavior - is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation;without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature.

关键词： automatic documentation generation source code summarization code comment generation

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Neural Comment Generation for source code with Auxiliary code Classification Task 26

Neural Comment Generation for Source Code with Auxiliary Cod...

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26th Asia-Pacific Software Engineering Conference (APSEC)

作者： Chen, Minghao Wan, Xiaojun Peking Univ Ctr Data Sci Wangxuan Inst Comp Technol MOE Key Lab Computat Linguist Beijing Peoples R China

ISBN: (纸本)9781728146485

code comments help program developers understand programs, read and navigate source code, thus resulting in more efficient software maintenance. Unfortunately, many codes are not commented adequately, or the code comments are missing. So developers have to spend additional time in reading source code. In this paper, we propose a new approach to automatically generating comments for source codes. Following the intuition behind the traditional sequence-to-sequence (Seq2Seq) model for machine translation, we propose a tree-to-sequence (Tree2Seq) model for code comment generation, which leverages an encoder to capture the structure information of source code. More importantly, code classification is involved as an auxiliary task for aiding the Tree2Seq model. We build a multi-task learning model to achieve this goal. We evaluate our models on a benchmark dataset with automatic metrics like BLEU, ROUGE, and METEOR. Experimental results show that our proposed Tree2Seq model outperforms traditional Seq2Seq model with attention, and our proposed multi-task learning model outperforms the state-of-the-art approaches by a substantial margin.

关键词： program comprehension source code summarization deep learning multi task learning

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Creating Important Statement Type Comments in Autocomment: Automatic Comment Generation Framework 3

Creating Important Statement Type Comments in Autocomment: A...

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3rd International Conference on Computer Science and Engineering (UBMK)

作者： Yildiz, Eren Ekin, Emine Huawei Huawei Turkcy R&D Ctr Istanbul Turkey Isik Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781538678930

This study explains important statement comments generated by Autocomment, which is an automatic comment generation framework described in [1]. To create the comments, the source code is not expected to be runnable, but to conliffm to the syntax rules of the Java programming language. Two types of comments are created: summary comments and important statement comments. How the important statements are extracted in a given Java source, how they are classified, how comments are generated for each type of important statement have been explained in this work.

关键词： source code summarization documentation generation program comprehension

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CrowdSummarizer Automated Generation of code Summaries for Java Programs through Crowdsourcing

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IEEE SOFTWARE 2017年第2期34卷 71-80页

作者： Badihi, Sahar Heydarnoori, Abbas Sharif Univ Technol Software Engn Tehran Iran Sharif Univ Technol Dept Comp Engn Tehran Iran

CrowdSummarizer exploits crowdsourcing, gamification, and natural-language processing to automatically generate high-level summaries of Java program methods. Researchers have implemented it as an Eclipse plug-in toget... 详细信息

关键词： Computer Games Crowdsourcing Java Natural Language Processing Software Maintenance source code Software Eclipse Plug In Software Engineering Web Based code summarization Game High Level Summaries Java Program Gamification Natural Language Processing Program source code Software Maintenance code Summary Automated Generation Crowd Summarizer Software Reliability Crowdsourcing Java Software Maintenance Software Engineering Software Measurement Crowd Summarizer Crowdsourcing Program Comprehension source code summarization Gamification Software Engineering Software Development

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Automatic Comment Generation using only source code 25

Automatic Comment Generation using only Source Code

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25th Signal Processing and Communications Applications Conference (SIU)

作者： Yildiz, Eren Ekin, Emine Isik Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781509064946

In this study, automatic comment generation for Java methods is described. It is sufficient that the codes conform to the syntax rules of the Java programming language, and it is not expected to be runnable. In order to generate comments, source code is examined syntactically. At this stage, only the method signature and its return type is needed. By working on open source Java projects, different templates have been developed for different method types. Using the compiled information which is the result of the examining source code that is currently being developed, the most suitable template is chosen and texts arc created. These texts explain the aim of the method. Created texts are added to source code as a comment.

关键词： source code summarization documentation generation program comprehension

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source code Documentation Generation Using Program Execution

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INFORMATION 2017年第4期8卷 148页

作者： Sulir, Matus Poruban, Jaroslav Tech Univ Kosice Fac Elect Engn & Informat Dept Comp & Informat Letna 9 Kosice 04200 Slovakia

Automated source code documentation approaches often describe methods in abstract terms, using the words contained in the static source code or code excerpts from repositories. In this paper, we describe DynamiDoc: a simple automated documentation generator based on dynamic analysis. Our representation-based approach traces the program being executed and records string representations of concrete argument values, a return value and a target object state before and after each method execution. Then, for each method, it generates documentation sentences with examples, such as "When called on [3, 1.2] with element = 3, the object changed to [1.2]". Advantages and shortcomings of the approach are listed. We also found out that the generated sentences are substantially shorter than the methods they describe. According to our small-scale study, the majority of objects in the generated documentation have their string representations overridden, which further confirms the potential usefulness of our approach. Finally, we propose an alternative, variable-based approach that describes the values of individual member variables, rather than the state of an object as a whole.

关键词： documentation generation source code summarization method documentation dynamic analysis examples

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