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Register a new userNeural Code Summarization: How Far Are We?
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Added by
Ensheng Shi
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Source code summaries are important for the comprehension and maintenance of programs. However, there are plenty of programs with missing, outdated, or mismatched summaries. Recently, deep learning techniques have been exploited to automatically generate summaries for given code snippets. To achieve a profound understanding of how far we are from solving this problem, in this paper, we conduct a systematic and in-depth analysis of five state-of-the-art neural source code summarization models on three widely used datasets. Our evaluation results suggest that: (1) The BLEU metric, which is widely used by existing work for evaluating the performance of the summarization models, has many variants. Ignoring the differences among the BLEU variants could affect the validity of the claimed results. Furthermore, we discover an important, previously unknown bug about BLEU calculation in a commonly-used software package. (2) Code pre-processing choices can have a large impact on the summarization performance, therefore they should not be ignored. (3) Some important characteristics of datasets (corpus size, data splitting method, and duplication ratio) have a significant impact on model evaluation. Based on the experimental results, we give some actionable guidelines on more systematic ways for evaluating code summarization and choosing the best method in different scenarios. We also suggest possible future research directions. We believe that our results can be of great help for practitioners and researchers in this interesting area.
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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.
Source code summaries are short natural language descriptions of code snippets that help developers better understand and maintain source code. There has been a surge of work on automatic code summarization to reduce the burden of writing summaries manually. However, most contemporary approaches mainly leverage the information within the boundary of the method being summarized (i.e., local context), and ignore the broader context that could assist with code summarization. This paper explores two global contexts, namely intra-class and inter-class contexts, and proposes the model CoCoSUM: Contextual Code Summarization with Multi-Relational Graph Neural Networks. CoCoSUM first incorporates class names as the intra-class context to generate the class semantic embeddings. Then, relevant Unified Modeling Language (UML) class diagrams are extracted as inter-class context and are encoded into the class relational embeddings using a novel Multi-Relational Graph Neural Network (MRGNN). Class semantic embeddings and class relational embeddings, together with the outputs from code token encoder and AST encoder, are passed to a decoder armed with a two-level attention mechanism to generate high-quality, context-aware code summaries. We conduct extensive experiments to evaluate our approach and compare it with other automatic code summarization models. The experimental results show that CoCoSUM is effective and outperforms state-of-the-art methods. Our source code and experimental data are available in the supplementary materials and will be made publicly available.
Software developers write a lot of source code and documentation during software development. Intrinsically, developers often recall parts of source code or code summaries that they had written in the past while implementing software or documenting them. To mimic developers code or summary generation behavior, we propose a retrieval augmented framework, REDCODER, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models. REDCODER has a couple of uniqueness. First, it extends the state-of-the-art dense retrieval technique to search for relevant code or summaries. Second, it can work with retrieval databases that include unimodal (only code or natural language description) or bimodal instances (code-description pairs). We conduct experiments and extensive analysis on two benchmark datasets of code generation and summarization in Java and Python, and the promising results endorse the effectiveness of our proposed retrieval augmented framework.
The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named ``embedder is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADG-Seq2Seq, yields significant improvements over existing state-of-the-art methods and maintains its performance as the length of the target code increases. Extensive ablation tests show that the proposed ADG embedding is effective and outperforms the baselines.
The software development community has been using code quality metrics for the last five decades. Despite their wide adoption, code quality metrics have attracted a fair share of criticism. In this paper, first, we carry out a qualitative exploration by surveying software developers to gauge their opinions about current practices and potential gaps with the present set of metrics. We identify deficiencies including lack of soundness, i.e., the ability of a metric to capture a notion accurately as promised by the metric, lack of support for assessing software architecture quality, and insufficient support for assessing software testing and infrastructure. In the second part of the paper, we focus on one specific code quality metric-LCOM as a case study to explore opportunities towards improved metrics. We evaluate existing LCOM algorithms qualitatively and quantitatively to observe how closely they represent the concept of cohesion. In this pursuit, we first create eight diverse cases that any LCOM algorithm must cover and obtain their cohesion levels by a set of experienced developers and consider them as a ground truth. We show that the present set of LCOM algorithms do poorly w.r.t. these cases. To bridge the identified gap, we propose a new approach to compute LCOM and evaluate the new approach with the ground truth. We also show, using a quantitative analysis using more than 90 thousand types belonging to 261 high-quality Java repositories, the present set of methods paint a very inaccurate and misleading picture of class cohesion. We conclude that the current code quality metrics in use suffer from various deficiencies, presenting ample opportunities for the research community to address the gaps.
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