Combining Word2Vec with Revised Vector Space Model for Better Code Retrieval

Nguyen, Thanh V.; Nguyen, Anh Tuan; Phan, Hung; Nguyen, Trong Duc; Nguyen, Tien N.

doi:10.1109/icse-c.2017.90

Cited by 28 publications

(10 citation statements)

References 12 publications

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“…In [76], word embeddings was combined with information retrieval to recommend similar bug reports. Methods similar to or based on word embeddings have also been used recently for better code retrieval [67], to find common software weaknesses [82], API recommendation [80] and sentiment analysis for software engineering [11]. Our work comple-ments the existing work as we build a cross-platform approach that leverages word embeddings to aid software-development-specific knowledge extraction tasks.…”

Section: Leveraging Word Embeddingsmentioning

confidence: 99%

SIEVE: Helping developers sift wheat from chaff via cross-platform analysis

et al. 2019

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Software developers have benefited from various sources of knowledge such as forums, question-and-answer sites, and social media platforms to help them in various tasks. Extracting software-related knowledge from different platforms involves many challenges. In this paper, we propose an approach to improve the effectiveness of knowledge extraction tasks by performing crossplatform analysis. Our approach is based on transfer representation learning and word embeddings, leveraging information extracted from a source platform which contains rich domain-related content. The information extracted is then used to solve tasks in another platform (considered as target platform) with less domain-related contents. We first build a word embeddings model as a representation learned from the source platform, and use the model to improve the performance of knowledge extraction tasks in the target platform. We experiment with Software Engineering Stack Exchange and Stack Overflow as source platforms, and two different target platforms, i.e., Twitter and YouTube. Our experiments show that our approach improves performance of existing work for the tasks of identifying software-related tweets and helpful YouTube comments.

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Section: Leveraging Word Embeddingsmentioning

confidence: 99%

SIEVE: Helping developers sift wheat from chaff via cross-platform analysis

et al. 2019

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“…In the literature, we can find several BoW based source code retrieval methods [6][7][8][9][10][11][12][13]. Authors have also reported using word2vec for software search [14][15][16][17], The researchers considered Correct at r C@r with abbreviation occurring in top r ranked positions and Pearson Correlation score to evaluate their work using popular data sets of Eclipse and AspectJ.…”

Section: Related Workmentioning

confidence: 99%

“…Emphasizing on the issue of handling mismatch in code search/retrieval using deep learning with Word2Vec was done by Van Nguyen et al [16], in which the researchers combined Word2Vec with Revised Vector Space model for better code retrieval (rVSM). rVSM computes the weight for a word based on a new term frequency-inverse document frequency (tf-idf) formula and a new scoring scheme among the vectors that takes documents' lengths into account.…”

Section: Related Workmentioning

confidence: 99%

Reusable Component Retrieval from a Large Repository Using Word2Vec with Continuous Bag of Words

Nagaraju¹,

Reddy²

2021

ISI

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A reusable code component is the one which can be easily used with a little or no adaptation to fit in to the application being developed. The major concern in such process is the maintenance of these reusable components in one place called ‘Repository’, so that those code components can be effectively identified as well as reused. Word embedding allows us to numerically represent our textual information. They have become so pervasive that almost all Natural Language Processing projects make use of them. In this work, we considered to use Word2Vec concept to find vector representation of features of a reusable component. The features of a reusable component in the form of sequence of words are input to Word2Vec network. Our method using Word2Vec with Continuous Bag of Words out performs existing method in the market. The proposed methodology has shown an accuracy of 94.8% in identifying the existing reusable component.

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“…More detailed information can be obtained through the analysis of local context (i.e. words that occur near each other), for example the skip-gram approach [45], which was previously applied to documentation from the Java Development Kit for code retrieval [46]. However, there is a danger predicting the context of one word at a time will miss information available through global statistics.…”

Section: Background and Related Workmentioning

confidence: 99%

Exploring Software Reusability Metrics with Q&A Forum Data

Patrick¹

2020

Preprint

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Question and answer (Q&A) forums contain valuable information regarding software reuse, but they can be challenging to analyse due to their unstructured free text. Here we introduce a new approach (LANLAN), using word embeddings and machine learning, to harness information available in StackOverflow. Specifically, we consider two different kinds of user communication describing difficulties encountered in software reuse: 'problem reports' point to potential defects, while 'support requests' ask for clarification on software usage. Word embeddings were trained on 1.6 billion tokens from StackOverflow and applied to identify which Q&A forum messages (from two large open source projects: Eclipse and Bioconductor) correspond to problem reports or support requests. LANLAN achieved an area under the receiver operator curve (AUROC) of over 0.9; it can be used to explore the relationship between software reusability metrics and difficulties encountered by users, as well as predict the number of difficulties users will face in the future. Q&A forum data can help improve understanding of software reuse, and may be harnessed as an additional resource to evaluate software reusability metrics.

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Combining Word2Vec with Revised Vector Space Model for Better Code Retrieval

Cited by 28 publications

References 12 publications

SIEVE: Helping developers sift wheat from chaff via cross-platform analysis

SIEVE: Helping developers sift wheat from chaff via cross-platform analysis

Reusable Component Retrieval from a Large Repository Using Word2Vec with Continuous Bag of Words

Exploring Software Reusability Metrics with Q&A Forum Data

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