Automatically Generating Natural Language Documentation for Methods

Newman, Christian D.; Dragan, Natalia; Collard, Michael L.; Maletic, Jonathan I.; Decker, Michael John; Guarnera, Drew T.; Abid, Nahla J.

doi:10.1109/dysdoc3.2018.00007

Cited by 4 publications

(1 citation statement)

References 12 publications

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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. [136][137][138][139][140][141][142][143][144][145][146][147] Code compilation, generation, translation and transformation.…”

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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Section: [49]mentioning

confidence: 99%

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

Subahi

2020

Computers

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Automated Java exceptions explanation using natural language generation techniques

Assiri

Elazhary

2020

Comp Applic In Engineering

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A common problem encountered by novice programmers and students taking introductory programming courses is how to understand exception messages and how to locate the corresponding errors. Such messages are typically too difficult for them to interpret without seeking help from colleagues or through online forums. Tackling this problem can help speed up their learning curves and hence increase their productivity. This paper proposes using natural language generation (NLG) techniques and the Rhetorical Structure Theory, which specifies how text spans are related and aggregated, to convert Java exception messages into readable and understandable natural language (NL) statements. An additional advantage is the possibility of adding more information and explanations that help students understand different exception types, their causes, and how they can be avoided. As a proof of concept, we developed Java Exceptions in the NL (JENL) tool to process Java exceptions using NLG techniques. Empirical evaluation has shown extreme satisfaction among Java programming subjects (including student novice programmers) with the capabilities of JENL as well as its usability.

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Topology of the Documentation Landscape

Raglianti¹

2022

2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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Automatically Generating Natural Language Documentation for Methods

Cited by 4 publications

References 12 publications

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

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

Automated Java exceptions explanation using natural language generation techniques

Topology of the Documentation Landscape

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