Supporting program comprehension with source code summarization

By Sonia Haiduc, Jairo Aponte, Andrian Marcus. 2010 ACM/IEEE 32nd International Conference on Software Engineering, 2010

328 citations recorded by the source corpus.

Subfields: Hardware and Architecture, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ef44c3316ddd9c8ab19a73b99b69161944e269b1

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Citations in the corpus

Automatic generation of natural language summaries for Java classes (2013, background): “Previous work on summarizing classes (and methods) investigated the use of TR techniques [5, 17] and found that they are not very well suited for this problem.”
Deep Code Comment Generation (2018, methodology): “Automatic code summarization approaches vary from manuallycrafted template [24, 35, 36], IR [14, 15, 43] to learning-based approaches [4, 19, 28].”
CodeSum: Translate Program Language to Natural Language (2017, methodology): “(Haiduc et al. 2010) describes one approach based on a Vector Space Model (VSM), in which a summary comprised of the n keywords with the highest term-frequency/inverse-document-frequency scores.”
Retrieval-based Neural Source Code Summarization (2020, methodology): “As described above, IR-based methods can effectively leverage the existing terms of the original code or summaries of similar code snippets [13, 18, 19, 46, 60, 61], while NMT-based methods generate summaries word-by-word from the whole corpus by directly maximizing the likelihood of the next word…”
On the Use of Automated Text Summarization Techniques for Summarizing Source Code (2010, methodology): “The TR based approaches for source code summarization, first introduced in [24], which we evaluate in this study, focus on summarizing whole software artifacts, with the purpose of aiding developers in comprehension tasks.”
Learning to Spot and Refactor Inconsistent Method Names (2019, methodology): “[83] used natural language summarization techniques and the lexical and structural context in code to improve code comprehension [84].”
CoCoGUM: Contextual Code Summarization with Multi-Relational GNN on UMLs (2020, methodology): “Information Retrieval (IR) based approaches use similar code snippets to select or synthesize a comment [5], and thus lack the capability of learning semantic meanings from existing code.”
A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes (2017, background): “One of these attempts is automatic summarization, which aims to generate a compact representation of the source code in a portion of natural language (Haiduc et al., 2010).”
A Novel Neural Source Code Representation Based on Abstract Syntax Tree (2019, methodology): “Many software engineering methods, such as source code classiﬁcation [1], [2], code clone detection [3], [4], [5], [6], defect prediction [7], [8] and code summarization [9], [10] have been proposed to improve software development and maintenance.”
A Hidden Markov Model to detect coded information islands in free text (2013, background): “Traceability recovery [6], impact analysis [9], bug report assignment [10], [11], code/text summarization [12], [13] are approaches that could benefit from methods that are able to discriminate noisy data and relevant information.”
Summarizing Source Code using a Neural Attention Model (2016, methodology): “There is also work on improving program comprehension (Haiduc et al., 2010), identifying cross-cutting source code concerns (Rastkar et al., 2011), and summarizing software bug reports (Rastkar et al., 2010).”
Autofolding for Source Code Summarization (2014, background): “[14], [15] and the follow up work by Eddy et al.”
A Neural Model for Generating Natural Language Summaries of Program Subroutines (2019, background): “2) Heuristic/Template-based: Haiduc et al. [11], [36] is often cited as the ﬁrst attempt to create text summaries of code, and indeed is the ﬁrst to introduce the term “source code summarization.””
Suggesting accurate method and class names (2015, background): “They later developed summaries that are used to improve comprehension [22].”