Automatic Generation of Program Comments Based on Problem Statements for Computational Thinking

Takahashi, Akiyoshi; Shiina, Hiromitsu; Kobayashi, Nobuyuki

doi:10.1109/iiai-aai.2019.00132

Cited by 3 publications

(7 citation statements)

References 7 publications

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“…Code-todescription Documentation generation [21], [34], [44], [66], [69], [83], [4], [89], [95] Code-to-code APR [31], [38], [43], [71], [85],…”

Section: Description-tocodementioning

confidence: 99%

“…RNN LSTM [21], [24], [34], [43], [44], [47], [50], [66], [67], [73], [97], [111] GRU [31], [69] Other [18], [114] Transformer [2] [47], [75], [76], [79], [83], [85], [4], [89], [91], [95], [102], [114] CNN [24], [29], [38], [66], [69], [89], [114] Other neural MLP [59], [63] Graph NN [69] Neural trees…”

Section: Model Type Sub-type Studiesmentioning

confidence: 99%

“…Word-based Subword-based RNN [21], [24], [31], [43], [44], [114] [47], [66], [67], [69], [89], [97] [18], [34], [50], [73], [111] Transformer [2] [75], [76], [91], [114] [79], [83], [85], [4], [95], [102] CNN [38] [29]…”

Section: Model Type Tokenizer Type Non-string Data Character-basedmentioning

confidence: 99%

“…A lack of available data led five studies [26], [44], [53], [59], [114] to manually create data. For example, [75] manually wrote translations in a target programming language given code from a source programming language.…”

Section: Description-tocodementioning

confidence: 99%

“…An alternative to evaluation by comparing synthesized code with code snippet is human evaluation. This is done by [4], [43], [44], [95]. Manually generating or evaluating data is time-intensive which is why automatic data mining or generation and automatic evaluation are more common.…”

Section: E Quality Of Available Datamentioning

confidence: 99%

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Code Generation Using Machine Learning: A Systematic Review

et al. 2022

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Recently, machine learning (ML) methods have been used to create powerful language models for a broad range of natural language processing tasks. An important subset of this field is that of generating code of programming languages for automatic software development. This review provides a broad and detailed overview of studies for code generation using ML. We selected 37 publications indexed in arXiv and IEEE Xplore databases that train ML models on programming language data to generate code. The three paradigms of code generation we identified in these studies are description-to-code, code-todescription, and code-to-code. The most popular applications that work in these paradigms were found to be code generation from natural language descriptions, documentation generation, and automatic program repair, respectively. The most frequently used ML models in these studies include recurrent neural networks, transformers, and convolutional neural networks. Other neural network architectures, as well as non-neural techniques, were also observed. In this review, we have summarized the applications, models, datasets, results, limitations, and future work of 37 publications. Additionally, we include discussions on topics general to the literature reviewed. This includes comparing different model types, comparing tokenizers, the volume and quality of data used, and methods for evaluating synthesized code. Furthermore, we provide three suggestions for future work for code generation using ML.

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“…Code-todescription Documentation generation [21], [34], [44], [66], [69], [83], [4], [89], [95] Code-to-code APR [31], [38], [43], [71], [85],…”

Section: Description-tocodementioning

confidence: 99%

Section: Model Type Sub-type Studiesmentioning

confidence: 99%

Section: Model Type Tokenizer Type Non-string Data Character-basedmentioning

confidence: 99%

Section: Description-tocodementioning

confidence: 99%

Section: E Quality Of Available Datamentioning

confidence: 99%

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Code Generation Using Machine Learning: A Systematic Review

et al. 2022

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Review of ways to apply machine learning methods in software engineering

Asaad,

Аvksentieva

2023

E3S Web Conf.

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This article reviews the integration of machine learning (ML) techniques into Software Engineering (SE) across various phases of the software development life cycle (SDLC). The purpose is to investigate the applications of ML in SE, analyze its methodologies, present findings, and draw conclusions regarding its impact. The study categorized ML applications in SE and assessed the performance of various ML algorithms. Authors identified ML applications in SDLC phases, including requirements analysis, design, implementation, testing, and maintenance. ML algorithms, such as supervised and unsupervised learning, are employed for tasks like software requirement identification, design pattern recognition, code generation, and automated testing. In summary, we find that ML-based techniques are experiencing a substantial surge in adoption within the field of software engineering. Nevertheless, it is evident that substantial endeavors are needed to establish thorough comparisons and synergies among these approaches, perform meaningful evaluations grounded in detailed real-world implementations that are applicable to industrial software development. Therefore, our key takeaway is the necessity for a shift in focus towards reproducible research, prioritizing this over isolated novel concepts. Failure to do so may result in the limited practical implementation of these promising applications.

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