Parameter-free probabilistic API mining across GitHub

Fowkes, Jaroslav; Sutton, Charles

doi:10.1145/2950290.2950319

Cited by 94 publications

(71 citation statements)

References 57 publications

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“…where each pn is the precision of n-grams, that is, the ratio of length n subsequences in the candidate that are also in the reference: pn = # n-grams appear in the reference+1 # n-grams of candidate+1 for n = 1, ..., N (15) where N is the maximum number of grams we consider. We set N to 4, which is a common practice in the Machine Learning literature [43].…”

Section: Discussionmentioning

confidence: 99%

“…To implement a certain functionality, for example, how to parse XML files, developers often reuse existing class libraries or frameworks by invoking the corresponding APIs. Obtaining which APIs to use, and their usage sequence (the method invocation sequence among the APIs) is very helpful in this regard [15,46,49]. For example, to "parse XML Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

“…Many developers search APIs from general web search engines such as Google and Bing. Developers can also perform a code search over an open source repository such as GitHub [3] and then utilize an API usage pattern miner [15,46,49] to obtain the appropriate API sequences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep API learning

Zhang

et al. 2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

478

272

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Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences. These approaches treat queries and APIs as bags-of-words and lack a deep understanding of the semantics of the query.We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query. Instead of a bag-of-words assumption, it learns the sequence of words in a query and the sequence of associated APIs. DeepAPI adapts a neural language model named RNN Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length context vector, and generates an API sequence based on the context vector. We also augment the RNN Encoder-Decoder by considering the importance of individual APIs. We empirically evaluate our approach with more than 7 million annotated code snippets collected from GitHub. The results show that our approach generates largely accurate API sequences and outperforms the related approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep API learning

Zhang

et al. 2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

478

272

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“…Gu et al [76] map natural language text to commonly used API sequences, Allamanis and Sutton [12] learn fine-grained source code idioms, that may include APIs. Fowkes and Sutton [63] uses a graphical model to mine interesting API sequences.…”

Section: Documentation Traceability and Information Retrievalmentioning

confidence: 99%

A Survey of Machine Learning for Big Code and Naturalness

et al. 2018

Self Cite

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Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit code's abundance of patterns. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss crosscutting and application-specific challenges and opportunities.1 It may be worth pointing out that deep learning and probabilistic modeling are not mutually exclusive. Indeed, many of the currently most effective methods for language modeling, for example, are based on deep learning. of probabilistic source code models (Section 5). Finally, we mention a few overlapping research areas (Section 7), and we discuss challenges and interesting future directions (Section 6).Related Reviews and other Resources. There have been short reviews summarizing the progress and the vision of the research area, from both software engineering [52] and programming languages perspectives [28,195]. However, none of these articles can be considered extensive literature reviews, which is the purpose of this work. Ernst [57] discusses promising areas of applying natural language processing to software development, including error messages, variable names, code comments, and user questions. Some resources, datasets and code can be found at http://learnbigcode.github.io/. An online version of the work reviewed here -which we will keep up-to-date by accepting external contributions -can be found at https://ml4code.github.io. THE NATURALNESS HYPOTHESISMany aspects of code, such as names, formatting, the lexical order of methods, etc. have no impact on program semantics. This is precisely why we abstract them in most program analyses. But then, why should statistical properties of code matter at all? To explain this, we recently suggested a hypothesis, called the naturalness hypothesis. The inspiration for the naturalness hypothesis can be traced back to the "literate programming" concept of D. Knuth, which draws from the insight that programming is a form of human communication: "Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do... " [105] The naturalness hypothesis, then, holds thatThe naturalness hypothesis. Software is a form of human communication; software corpora have similar statistical properties to natural language corpora; and these properties can be exploited to build better software engineering tools.The exploitation of the statistics of human communication is a mature and effective technology, with numerous applications ...

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“…Naturalize [1] enforces a consistent naming style. Other applications of statistical NLP include code suggestion [9], [14], code convention [1], method name suggestion [2], API suggestions [18], code mining [3], type resolution [15], pattern mining [6]. Statistical NLP was used to generate code from text, e.g., SWIM [16], DeepAPI [7], Anycode [8], etc.…”

Section: Related Workmentioning

confidence: 99%

Recovering Variable Names for Minified Code with Usage Contexts

Tran

Nguyen

et al. 2019

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

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To avoid the exposure of original source code in a Web application, the variable names in JS code deployed in the wild are often replaced by short, meaningless names, thus making the code extremely difficult to manually understand and analysis. This paper presents JSNEAT, an information retrieval (IR)-based approach to recover the variable names in minified JS code. JSNEAT follows a data-driven approach to recover names by searching for them in a large corpus of open-source JS code. We use three types of contexts to match a variable in given minified code against the corpus including the context of the properties and roles of the variable, the context of that variable and relations with other variables under recovery, and the context of the task of the function to which the variable contributes. We performed several empirical experiments to evaluate JSNEAT on the dataset of more than 322K JS files with 1M functions, and 3.5M variables with 176K unique variable names. We found that JSNEAT achieves a high accuracy of 69.1%, which is the relative improvements of 66.1% and 43% over two state-of-theart approaches JSNice and JSNaughty, respectively. The time to recover for a file or a variable with JSNEAT is twice as fast as with JSNice and 4x as fast as with JNaughty, respectively.

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Parameter-free probabilistic API mining across GitHub

Cited by 94 publications

References 57 publications

Deep API learning

Deep API learning

A Survey of Machine Learning for Big Code and Naturalness

Recovering Variable Names for Minified Code with Usage Contexts

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