Automatic translation from pseudocode to source code: A conceptual-metamodel approach

Dirgahayu, Teduh; Huda, Sheila Nurul; Zukhri, Zainudin; Ratnasari, Chanifah Indah

doi:10.1109/cyberneticscom.2017.8311696

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…The problem with this method is that it presents a complex solution to a simple problem. As seen in previous 2 papers [14] & [15], the same output can be achieved with simple one-to-one mapping.…”

Section: F Modified Co-occurrence Matrix Techniquementioning

confidence: 53%

“…Teduh Dirgahayu et.al. [14] proposed a method to automatically convert pseudocode to Source-code. In this method, the pseudocode is first translated to an intermediate model then to source-code.…”

Section: F Modified Co-occurrence Matrix Techniquementioning

confidence: 99%

“…Once the pseudocode file is ready, the only thing left to do is to convert it into a programming language. Using techniques that convert XML to program [14] [15], we can easily convert pseudocode to source-code. An automatic pseudocode generation is independent in creating a language specific source-code.…”

Section: Pseudocode To Language Specific Source-codementioning

confidence: 99%

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NLI-GSC: A Natural Language Interface for Generating SourceCode

Ansari¹,

Vora²

2022

IJACSA

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There are many different programming languages and each programming language has its own structure or way of writing the code, it becomes difficult to learn and frequently switch between different programming languages. Due to this reason, a person working with multiple programming languages needs to look at documentations frequently which costs time and effort. In the past few years, there have been significant increase in the amount of papers published on this topic, each providing a unique solution to this problem. Many of these papers are based on applying NLP concepts in unique configuration to get the desired results. Some have used AI along with NLP to train the system to generate source-code in specific language, and some have trained the AI directly without pre-processing the dataset with NLP. All of these papers face two problems: a lack of proper dataset for this particular application and each paper can convent natural language into only one specified programming language source-code. This proposed system shows that a language independent solution is a feasible alternate for writing source-code without having full knowledge about a programming language. The proposed system uses Natural Language Processing to convert Natural Language into programming language-independent pseudo code using custom Named Entity Recognition and save it in XML (eXtensible Markup Language) format which is an intermediate step. Then, using traditional programming, this system converts the generated pseudo code into programming language-dependent source-code. In this paper, another novel method has been proposed to create dataset from scratch using predefined structure that is filled with predefined keywords creating unique combination of training dataset.

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Section: F Modified Co-occurrence Matrix Techniquementioning

confidence: 53%

Section: F Modified Co-occurrence Matrix Techniquementioning

confidence: 99%

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NLI-GSC: A Natural Language Interface for Generating SourceCode

Ansari¹,

Vora²

2022

IJACSA

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“…By using semantic role labeling [19] with more semantic roles, the issues mentioned above could be solved. Teduh Dirgahayu et al [20] presented a conceptualmetamodeling approach that uses model-driven engineering (MDE) [21] to develop an automatic translation tool for translating pseudocode into source code. By introducing an intermediate model, this translation tool can disjoin the pseudocode statements in XML.…”

Section: Literature Reviewmentioning

confidence: 99%

Sequence-to-Sequence Learning-Based Conversion of Pseudo-Code to Source Code Using Neural Translation Approach

et al. 2022

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Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm's correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with stateof-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy.

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“…6 It is necessary for the developers to understand the source code, and to take an advanced approach to reorganizing the collected data in a way that can be easily inferred and understood by the developers. 7 The process of searching for the location of features in code is normally carried out by the developers. Adding or missing any irrelevant term to any query or corpus may negatively impact the whole searching process, which increases a false positive in the results, and consequently wastes the developers' time: All weak results require the re-writing and re-formulating of the query in order to obtain relevant and accurate results.…”

Section: Introductionmentioning

confidence: 99%

Feature location enhancement based on source code augmentation with synonyms of terms

Saifan

Obeidat

2020

Softw Pract Exp

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SummaryIn software maintenance the developers may add new feature to program, improve existing function, and remove bugs. In this case the developer should identify the location in the source code that corresponds to a specific functionality; this is known as feature location. This presents a new approach for enhancing the process of feature location using Information Retrieval (IR) and Natural Language Processing. The approach presented augments the source code with additional semantic information that was extracted and derived from the synonyms of source code terms. This approach works in a pipeline structure, starting by augmenting the source code corpus with synonyms of the original terms and ending by inferring the source code with a particular user query. More specifically, the WordNet platform is used for extracting the synonyms of terms. Moreover, the approach uses an advanced IR technique, namely the Latent Semantic Indexing, for searching and inferring the source code. The used approach was tested and evaluated on two open source systems, namely the Qt and Hippodraw. Four experiments were conducted on each system using 21 features and the results showed that enriching the source code with synonyms of terms clearly and significantly improved the process of feature location efficiently. The experimental results showed that the approach presented obtained higher levels of Recall and Precision. For that reason, it has been shown to improve the state‐of‐the‐art techniques for feature location process.

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Automatic translation from pseudocode to source code: A conceptual-metamodel approach

Cited by 7 publications

References 9 publications

NLI-GSC: A Natural Language Interface for Generating SourceCode

NLI-GSC: A Natural Language Interface for Generating SourceCode

Sequence-to-Sequence Learning-Based Conversion of Pseudo-Code to Source Code Using Neural Translation Approach

Feature location enhancement based on source code augmentation with synonyms of terms

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