What makes a good code example?: A study of programming Q&amp;amp;A in StackOverflow

Nasehi, Seyed Mehdi; Sillito, Jonathan; Maurer, Frank; Burns, Chris

doi:10.1109/icsm.2012.6405249

Cited by 234 publications

(149 citation statements)

References 27 publications

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“…Currently an average of 7000 questions are posted on the site daily, and as of August 2010, StackOverflow had an answer rate above 90% and a median answer time of only 11 minutes [42]. According to Nasehi et al [43], as of February 2012, the median time of accepted answers being posted on the site was 24 minutes and in the first hour 70% of questions received their first answer. Before the advent of social Q&A websites, online forums and threaded discussions were the most commonly used mechanism for Q&A.…”

Section: Accepted For Publicationmentioning

confidence: 99%

Automatic mapping of user tags to Wikipedia concepts: The case of a Q&A website – StackOverflow

Joorabchi

English

Mahdi

2015

Journal of Information Science

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The uncontrolled nature of user-assigned tags makes them prone to various inconsistencies caused by spelling variations, synonyms, acronyms, and hyponyms. These inconsistencies in turn lead to some of the common problems associated with the use of folksonomies such as the tag explosion phenomenon. Mapping user tags to their corresponding Wikipedia articles, as well-formed concepts, offers multi-facet benefits to the process of subject metadata generation and management in a wide range of online environments. These include normalization of inconsistencies, elimination of personal tags, and improvement of the interchangeability of existing subject metadata. In this article, we propose a machine learning-based method capable of automatic mapping of user tags to their equivalent Wikipedia concepts. We have demonstrated the application of the proposed method and evaluated its performance using the currently most popular computer programming Q&A website, StackOverflow.com, as our test platform. Currently, around 20 million posts in StackOverflow are annotated with about 37,000 unique user tags, from which we have chosen a subset of 1,256 tags to evaluate the accuracy performance of our proposed mapping method. We have evaluated the performance of our method using the standard information retrieval measures of precision, recall, and F1. Depending on the machine learning-based classification algorithm used as part of the mapping process, F1 scores as high as 99.6% were achieved.

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Section: Accepted For Publicationmentioning

confidence: 99%

Automatic mapping of user tags to Wikipedia concepts: The case of a Q&A website – StackOverflow

Joorabchi

English

Mahdi

2015

Journal of Information Science

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“…Nasehi et al investigated the characteristics of code examples in highly rated answers on Stack Overflow [23]. They found that these examples tend to be "concise": the examples are typically less than four lines and "shorter than similar code inside other answers to the same question", with "reduced complexity" and "unnecessary details" left out.…”

Section: Related Workmentioning

confidence: 99%

“…Concise examples also tend to be in highly rated answers on the developer forum Stack Overflow [23]. In contrast, longer code examples can be difficult to understand [29] or even be misleading [5], and cause serious presentation problems for summarizing documents, e.g., in web search results [34].…”

Section: Introductionmentioning

confidence: 99%

Selection and presentation practices for code example summarization

Ying

Robillard

2014

Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

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Code examples are an important source for answering questions about software libraries and applications. Many usage contexts for code examples require them to be distilled to their essence: e.g., when serving as cues to longer documents, or for reminding developers of a previously known idiom. We conducted a study to discover how code can be summarized and why. As part of the study, we collected 156 pairs of code examples and their summaries from 16 participants, along with over 26 hours of think-aloud verbalizations detailing the decisions of the participants during their summarization activities. Based on a qualitative analysis of this data we elicited a list of practices followed by the participants to summarize code examples and propose empirically-supported hypotheses justifying the use of specific practices. One main finding was that none of the participants exclusively extracted code verbatim for the summaries, motivating abstractive summarization. The results provide a grounded basis for the development of code example summarization and presentation technology.

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“…Stack Overflow: Several studies have explored Stack Overflow questions and answers [1,7,30,36]. However, to the best of our knowledge, its data has never been leveraged to improve code search engine results.…”

Section: Miscellaneousmentioning

confidence: 99%

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

et al. 2018

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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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What makes a good code example?: A study of programming Q&A in StackOverflow

Cited by 234 publications

References 27 publications

Automatic mapping of user tags to Wikipedia concepts: The case of a Q&A website – StackOverflow

Automatic mapping of user tags to Wikipedia concepts: The case of a Q&A website – StackOverflow

Selection and presentation practices for code example summarization

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

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