Toward Automatic Summarization of Arbitrary Java Statements for Novice Programmers

Hassan, Mohammed; Hill, Emily

doi:10.1109/icsme.2018.00063

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…; similarityEuclidean obtains values in the range (0, 1], where 1 means that the objects are the same and values close to 0 means that the objects are highly dissimilar. The cosine measure was modified by simply adding a unit to obtain only positive values: modifiedCosine = cosine + 1 in the range [1,2]. Finally, the similarity between two objects is given by modifiedEuclidean * modifyCosine.…”

Section: A Partitional Clustering Representationmentioning

confidence: 99%

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Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords

et al. 2020

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The automatic text summarization (ATS) task consists in automatically synthesizing a document to provide a condensed version of it. Creating a summary requires not only selecting the main topics of the sentences but also identifying the key relationships between these topics. Related works rank text units (mainly sentences) to select those that could form the summary. However, the resulting summaries may not include all the topics covered in the source text because important information may have been discarded. In addition, the semantic structure of documents has been barely explored in this field. Thus, this study proposes a new method for the ATS task that takes advantage of semantic information to improve keyword detection. This proposed method increases not only the coverage by clustering the sentences to identify the main topics in the source document but also the precision by detecting the keywords in the clusters. The experimental results of this work indicate that the proposed method outperformed previous methods with a standard collection.

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Section: A Partitional Clustering Representationmentioning

confidence: 99%

“…For example, in the work of Hassan and Hill [1], automatic summarization techniques were used to provide comments for programming language statements. Their method establishes the basic concepts of a system that helps in understanding large codes, generally not commented on, written by other programmers.…”

Section: Introductionmentioning

confidence: 99%

Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords

et al. 2020

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“…Applications [116][117][118][119][120][121][122][123][124][125] Code summarization. [126][127][128][129][130][131][132][133][134][135] Code documentation and comments generation.…”

Section: [49]mentioning

confidence: 99%

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Subahi

2020

Computers

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Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks.

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“…Racchetti et al proposed an approach to automatically generate documentation for Programmable Logic Controller (PLC) code [23]. Hassan and Hill presented a technique towards automatically generating comments for Java statements suitable for novice programmers [24]. Moreover, prior studies proposed an approach to summarise individual code changes [25] and the software evolution based on code changes [26].…”

Section: Related Workmentioning

confidence: 99%

Automatically Generating Documentation for Lambda Expressions in Java

Alqaimi

Thongtanunam

2019

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

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When lambda expressions were introduced to the Java programming language as part of the release of Java 8 in 2014, they were the language's first step into functional programming. Since lambda expressions are still relatively new, not all developers use or understand them. In this paper, we first present the results of an empirical study to determine how frequently developers of GitHub repositories make use of lambda expressions and how they are documented. We find that 11% of Java GitHub repositories use lambda expressions, and that only 6% of the lambda expressions are accompanied by source code comments. We then present a tool called LAMBDADOC which can automatically detect lambda expressions in a Java repository and generate natural language documentation for them. Our evaluation of LAMBDADOC with 23 professional developers shows that they perceive the generated documentation to be complete, concise, and expressive, while the majority of the documentation produced by our participants without tool support was inadequate. Our contribution builds an important step towards automatically generating documentation for functional programming constructs in an object-oriented language.

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Toward Automatic Summarization of Arbitrary Java Statements for Novice Programmers

Cited by 6 publications

References 20 publications

Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords

Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Automatically Generating Documentation for Lambda Expressions in Java

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