Popularity, Interoperability, and Impact of Programming Languages in 100,000 Open Source Projects

Bissyandé, Tegawendé F.; Thung, Ferdian; Lo, David; Jiang, Lingxiao; Réveillère, Laurent

doi:10.1109/compsac.2013.55

“…As JavaScript shows a high connectivity with Ruby and PHP [20], JavaScript can fetch a high fork count from developers. From the project development perspective, the topic domain or field interest to developers, and the selective programming languages, contribute to the high fork frequency.…”

Section: Scenario 4: Javascript High Fork Visibility With Highly Adopmentioning

confidence: 99%

Predicting Fork Visibility Performance on Programming Language Interoperability in Open Source Projects

Chua¹,

Bernardo²

2018

ijacsa

0

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Abstract-Despite a variety of programming languages adopted in open source (OS) projects, fork variation on some languages has been minimal and slow to be adopted, and there is little research as to why this is so. We therefore employed a Knearest neighbours (KNN) technique to predict the fork visibility performance of a productive language from a pool of programming languages adopted in projects. In total, 38 showcase OS projects from 2012 to 2016 were downloaded from the GitHub website and categorized into different levels of programming language adoption clusters. Among 33 languages, JavaScript is one of the popular languages that adopted by community. It has been predicted the language chosen when fork visibility is high can increase project longevity as a highly visible language is likely to occur more often in projects with a significant number of interoperable programming languages and high language fork count. Conversely, a low fork language reduces longevity in projects with an insignificant number of interoperable programming languages and low fork count. Our results reveal the survival of a productive language is in response to high language visibility (large fork number) and high interoperability of multiple programming languages.

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“…Java was selected in this instantiation since it is one of the most popular programming languages and represents a large developer base [8]. In this work, we made use of the posts.xml documents that have an actual post (i.e., question and answer pair) and other associated metadata such as tags, creation date, question ID, view count of the post, and score of answers.…”

Section: The Gitsearch Code Search Enginementioning

confidence: 99%

“…To build GitSearch, we selected Stack Overflow as the Q&A site where to retrieve relevant developer-approved code snippets. For the search proxy, our implementation directly leverages Google web search 8 . User queries are sent to Google Search for retrieving all relevant Q&A posts (i.e., text similarity matching).…”

Section: The Gitsearch Code Search Enginementioning

confidence: 99%

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Augmenting and structuring user queries to support efficient free-form code search

Sirres

¹

,

Bissyandé

²

,

Kim

³

et al. 2018

Self Cite

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Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and Stack Overflow Q&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers; (2) the results are qualitatively better than those of existing Internet-scale code search engines; (3) our engine is competitive against web search engines, such as Google, in helping users complete solve programming tasks; and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for Stack Overflow questions.

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“…The current implementation of CoCaBu uses the scoring function implemented in the Lucene library. This function combines the Boolean Model (BM) and the Vector Space Model (VSM) to determine the relevancy of a document given for a user query 8 . BM is used for reducing the amount of documents that need to be scored by using Boolean logic in the query specification.…”

Section: Code Search Enginementioning

confidence: 99%

Augmenting and structuring user queries to support efficient free-form code search

Sirres

¹

,

Bissyandé

²

,

Kim

³

et al. 2018

Proceedings of the 40th International Conference on Software Engineering

Self Cite

View full text Add to dashboard Cite

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and Stack Overflow Q&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers; (2) the results are qualitatively better than those of existing Internet-scale code search engines; (3) our engine is competitive against web search engines, such as Google, in helping users complete solve programming tasks; and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for Stack Overflow questions.

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Popularity, Interoperability, and Impact of Programming Languages in 100,000 Open Source Projects

Cited by 89 publications

References 11 publications

Predicting Fork Visibility Performance on Programming Language Interoperability in Open Source Projects

Predicting Fork Visibility Performance on Programming Language Interoperability in Open Source Projects

Augmenting and structuring user queries to support efficient free-form code search

Augmenting and structuring user queries to support efficient free-form code search

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