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An empirical study of the textual similarity between source code and source code summaries

EMPIRICAL SOFTWARE ENGINEERING 2016年第1期21卷 17-42页

作者： McBurney, Paul W. McMillan, Collin Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

Source code documentation often contains summaries of source code written by authors. Recently, automatic source code summarization tools have emerged that generate summaries without requiring author intervention. These summaries are designed for readers to be able to understand the high-level concepts of the source code. Unfortunately, there is no agreed upon understanding of what makes up a "good summary." This paper presents an empirical study examining summaries of source code written by authors, readers, and automatic source code summarization tools. This empirical study examines the textual similarity between source code and summaries of source code using Short Text Semantic Similarity metrics. We found that readers use source code in their summaries more than authors do. Additionally, this study finds that accuracy of a human written summary can be estimated by the textual similarity of that summary to the source code.

关键词： Source code summarization documentation Textual similarity automatic documentation generation

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Function Call Graph Context Encoding for Neural Source Code Summarization

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第9期49卷 4268-4281页

作者： Bansal, Aakash Eberhart, Zachary Karas, Zachary Huang, Yu Mcmillan, Collin Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA Univ Vanderbilt Dept Comp Sci Tennessee IL USA

Source code summarization is the task of writing natural language descriptions of source code. The primary use of these descriptions is in documentation for programmers. automatic generation of these descriptions is a high value research target due to the time cost to programmers of writing these descriptions themselves. In recent years, a confluence of software engineering and artificial intelligence research has made inroads into automatic source code summarization through applications of neural models of that source code. However, an Achilles' heel to a vast majority of approaches is that they tend to rely solely on the context provided by the source code being summarized. But empirical studies in program comprehension are quite clear that the information needed to describe code much more often resides in the context in the form of Function Call Graph surrounding that code. In this paper, we present a technique for encoding this call graph context for neural models of code summarization. We implement our approach as a supplement to existing approaches, and show statistically significant improvement over existing approaches. In a human study with 20 programmers, we show that programmers perceive generated summaries to generally be as accurate, readable, and concise as human-written summaries.

关键词： Codes Source coding Context modeling Decoding Algorithms Software engineering Machine translation automatic documentation generation context-aware models neural networks source code summarization

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30th IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Haque, Sakib Eberhart, Zachary Bansal, Aakash McMillan, Collin Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9781450392983

Source code summarization involves creating brief descriptions of source code in natural language. These descriptions are a key component of software documentation such as JavaDocs. automatic code summarization is a prized target of software engineering research, due to the high value summaries have to programmers and the simultaneously high cost of writing and maintaining documentation by hand. Current work is almost all based on machine models trained via big data input. Large datasets of examples of code and summaries of that code are used to train an e.g. encoder-decoder neural model. Then the output predictions of the model are evaluated against a set of reference summaries. The input is code not seen by the model, and the prediction is compared to a reference. The means by which a prediction is compared to a reference is essentially word overlap, calculated via a metric such as BLEU or ROUGE. The problem with using word overlap is that not all words in a sentence have the same importance, and many words have synonyms. The result is that calculated similarity may not match the perceived similarity by human readers. In this paper, we conduct an experiment to measure the degree to which various word overlap metrics correlate to human-rated similarity of predicted and reference summaries. We evaluate alternatives based on current work in semantic similarity metrics and propose recommendations for evaluation of source code summarization.

关键词： source code summarization automatic documentation generation evaluation metrics

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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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Ensemble Models for Neural Source Code Summarization of Subroutines 37

Ensemble Models for Neural Source Code Summarization of Subr...

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37th IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： LeClair, Alexander Bansal, Aakash McMillan, Collin Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9781665428828

A source code summary of a subroutine is a brief description of that subroutine. Summaries underpin a majority of documentation consumed by programmers, such as the method summaries in JavaDocs. Source code summarization is the task of writing these summaries. At present, most state-of-the-art approaches for code summarization are neural network-based solutions akin to seq2seq, graph2seq, and other encoder-decoder architectures. The input to the encoder is source code, while the decoder helps predict the natural language summary. While these models tend to be similar in structure, evidence is emerging that different models make different contributions to prediction quality - differences in model performance are orthogonal and complementary rather than uniform over the entire dataset. In this paper, we explore the orthogonal nature of different neural code summarization approaches and propose ensemble models to exploit this orthogonality for better overall performance. We demonstrate that a simple ensemble strategy boosts performance by up to 14.8%, and provide an explanation for this boost. The takeaway from this work is that a relatively small change to the inference procedure in most neural code summarization techniques leads to outsized improvements in prediction quality.

关键词： source code summarization automatic documentation generation neural networks

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Action Word Prediction for Neural Source Code Summarization 28

Action Word Prediction for Neural Source Code Summarization

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28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Haque, Sakib Bansal, Aakash Wu, Lingfei McMillan, Collin Univ Notre Dame Dept Comp Sci Notre Dame IN 46556 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781728196305

Source code summarization is the task of creating short, natural language descriptions of source code. Code summarization is the backbone of much software documentation such as JavaDocs, in which very brief comments such as "adds the customer object" help programmers quickly understand a snippet of code. In recent years, automatic code summarization has become a high value target of research, with approaches based on neural networks making rapid progress. However, as we will show in this paper, the production of good summaries relies on the production of the action word in those summaries: the meaning of the example above would be completely changed if "removes" were substituted for "adds." In this paper, we advocate for a special emphasis on action word prediction as an important stepping stone problem towards better code summarization - current techniques try to predict the action word along with the whole summary, and yet action word prediction on its own is quite difficult. We show the value of the problem for code summaries, explore the performance of current baselines, and provide recommendations for future research.

关键词： neural networks source code summarization automatic documentation generation AI in SE

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Label Smoothing Improves Neural Source Code Summarization 31

Label Smoothing Improves Neural Source Code Summarization

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31st IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Haque, Sakib Bansal, Aakash McMillan, Collin Univ Notre Dame Dept Comp Sci Notre Dame IN 46556 USA

ISBN: (纸本)9798350337501

Label smoothing is a regularization technique for neural networks. Normally neural models are trained to an output distribution that is a vector with a single 1 for the correct prediction, and 0 for all other elements. Label smoothing converts the correct prediction location to something slightly less than 1, then distributes the remainder to the other elements such that they are slightly greater than 0. A conceptual explanation behind label smoothing is that it helps prevent a neural model from becoming "overconfident" by forcing it to consider alternatives, even if only slightly. Label smoothing has been shown to help several areas of language generation, yet typically requires considerable tuning and testing to achieve the optimal results. This tuning and testing has not been reported for neural source code summarization - a growing research area in software engineering that seeks to generate natural language descriptions of source code behavior. In this paper, we demonstrate the effect of label smoothing on several baselines in neural code summarization, and conduct an experiment to find good parameters for label smoothing and make recommendations for its use.

关键词： Source code summarization automatic documentation generation label smoothing optimization

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Project-Level Encoding for Neural Source Code Summarization of Subroutines 29

Project-Level Encoding for Neural Source Code Summarization ...

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29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software Repositories (MSR)

作者： Bansal, Aakash Haque, Sakib McMillan, Collin Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9781665414036

Source code summarization of a subroutine is the task of writing a short, natural language description of that subroutine. The description usually serves in documentation aimed at programmers, where even brief phrase (e.g. "compresses data to a zip file") can help readers rapidly comprehend what a subroutine does without resorting to reading the code itself. Techniques based on neural networks (and encoder-decoder model designs in particular) have established themselves as the state-of-the-art. Yet a problem widely recognized with these models is that they assume the information needed to create a summary is present within the code being summarized itself - an assumption which is at odds with program comprehension literature. Thus a current research frontier lies in the question of encoding source code context into neural models of summarization. In this paper, we present a project-level encoder to improve models of code summarization. By project-level, we mean that we create a vectorized representation of selected code files in a software project, and use that representation to augment the encoder of state-of-the-art neural code summarization techniques. We demonstrate how our encoder improves several existing models, and provide guidelines for maximizing improvement while controlling time and resource costs in model size.

关键词： source code summarization automatic documentation generation neural networks

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Empirical Assessment of Software documentation Strategies: A Randomized Controlled Trial 19

Empirical Assessment of Software Documentation Strategies: A...

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Proceedings of the 50th ACM Technical Symposium on Computer Science Education

作者： Scott Kolodziej Texas A&M University College Station TX USA

ISBN: (纸本)9781450358903

Source code documentation is an important part of teaching students how to be effective programmers. But what evidence do we have to support what good documentation looks like? This study utilizes a randomized controlled trial to experimentally compare several different types of documentation, including traditional comments, self-documenting naming, and an automatic documentation generator. The results of this experiment show that the relationship between documentation and source code understanding is more complex than simply "more is better," and poorly documented code may even lead to a more correct understanding of the source code.

关键词： software documentation empirical study self-documenting code automatic documentation generation randomized controlled trial

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