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Parameter-free probabilistic API mining across GitHub

Jaroslav Fowkes, Charles Sutton
2016 Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2016  
To address this, we present PAM (Probabilistic API Miner), a near parameter-free probabilistic algorithm for mining the most interesting API call patterns.  ...  Existing API mining algorithms can be difficult to use as they require expensive parameter tuning and the returned set of API calls can be large, highly redundant and difficult to understand.  ...  To address this unsatisfactory state of affairs, we propose PAM (Probabilistic API Miner) 1 , a near parameter-free probabilistic algorithm for mining the most interesting API patterns.  ... 
doi:10.1145/2950290.2950319 dblp:conf/sigsoft/FowkesS16 fatcat:q5z3g57l3ne2jo5tlnkyreno2m

Mining idioms from source code

Miltiadis Allamanis, Charles Sutton
2014 Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2014  
We present HAGGIS, a system for mining code idioms that builds on recent advanced techniques from statistical natural language processing, namely, nonparametric Bayesian probabilistic tree substitution  ...  We apply HAGGIS to several of the most popular open source projects from GitHub.  ...  Probabilistic Grammars A probabilistic context free grammar (PCFG) is a simple way to define a distribution over the strings of a context-free language.  ... 
doi:10.1145/2635868.2635901 dblp:conf/sigsoft/AllamanisS14 fatcat:uxapgog4bff3piuujk5cx6d7cu

Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics

Mohammad Masudur Rahman, Chanchal K. Roy
2018 arXiv   pre-print
query keywords and the API classes.  ...  ., GitHub native search, Krugle). Such queries often do not lead to any relevant results due to vocabulary mismatch problems.  ...  On the contrary, we collect relevant API classes for free-form NL queries by mining crowd generated knowledge stored in Stack Overflow questions and answers.  ... 
arXiv:1807.08798v1 fatcat:zwyukulqnfhapaadbdzi36lnpa

A Survey of Machine Learning for Big Code and Naturalness [article]

Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton
2018 arXiv   pre-print
We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models.  ...  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  ...  Mining common API patterns is a recurring theme and there is a large literature of non-probabilistic methods (e.g. frequency-based) for mining and synthesizing API patterns [31, 178] , which are out-of-scope  ... 
arXiv:1709.06182v2 fatcat:hbvgyonqsjgq3nqwji6jf3aybe

A Survey of Machine Learning for Big Code and Naturalness

Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton
2018 ACM Computing Surveys  
We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models.  ...  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  ...  Mining common API patterns is a recurring theme and there is a large literature of non-probabilistic methods (e.g. frequency-based) for mining and synthesizing API patterns [34, 192] , which are out-of-scope  ... 
doi:10.1145/3212695 fatcat:iuuocyctg5adjmobhc2zw23rfu

Learning how to listen: Automatically finding bug patterns in event-driven JavaScript APIs [article]

Ellen Arteca, Max Schäfer, Frank Tip
2022 arXiv   pre-print
Many popular event-based APIs allow event names to be specified as free-form strings without any validation, potentially leading to lost events for which no listener has been registered and dead listeners  ...  Concentrating on the problem of detecting dead listeners, we present an approach to learn how to correctly use event-based APIs by first mining a large corpus of JavaScript code using a simple static analysis  ...  Moreover, a cross-validation experiment revealed the configuration parameters to be quite stable across subsets of the data.  ... 
arXiv:2107.13708v3 fatcat:et4w54zezzby5p5ddev5yxmv6q

Mining Semantic Loop Idioms

Miltiadis Allamanis, Earl T. Barr, Christian Bird, Premkumar Devanbu, Mark Marron, Charles Sutton
2018 IEEE Transactions on Software Engineering  
To write code, developers stitch together patterns, like API protocols or data structure traversals.  ...  Discovering these patterns can identify inconsistencies in code or opportunities to replace these patterns with an API or a language construct.  ...  Another related area is API mining [1, 50, 68, 66] .  ... 
doi:10.1109/tse.2018.2832048 fatcat:ihyoy2p3w5ejtj25bu2cjfsonm

Mining Hidden Populations through Attributed Search [article]

Suhansanu Kumar, Heting Gao, Changyu Wang, Hari Sundaram, Kevin Chen-Chuan Chang
2019 arXiv   pre-print
When the property of the target entities is not directly queryable via the API, we refer to the property as 'hidden' and the population as a hidden population.  ...  Researchers often query online social platforms through their application programming interfaces (API) to find target populations such as people with mental illness De-Choudhury2017 and jazz musicians  ...  We maintain the model of the entity database using a set of probabilistic parameters.  ... 
arXiv:1905.04505v1 fatcat:woavlvneizbo7dnq7y5akli2xu

Searching a Database of Source Codes Using Contextualized Code Search [article]

Rohan Mukherjee, Swarat Chaudhuri, Chris Jermaine
2020 arXiv   pre-print
The benefit of contextualized code search compared to other code search methods is that search happens "for free" using the surrounding context; the user need not specify the parameters for search.  ...  When extracting methods as evidence, CODEC considers the method's return type, its sequence of formal parameters (formal parameter type and name, split using camel-case), sequences of API calls made within  ... 
arXiv:2001.03277v1 fatcat:okkyy6vvfvd6dbtiotk2vjxes4

CODIT: Code Editing with Tree-Based Neural Models [article]

Saikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis, Baishakhi Ray
2020 arXiv   pre-print
[76] , [77] proposed statistical modeling technique for mining source code idioms, where they leverages probabilistic Tree Substitution Grammar (pTSG) for mining code idioms.  ...  Thus, our model resembles a Probabilistic Context-Free Grammar (PCFG), but the probability of each rule depends on its surroundings.  ... 
arXiv:1810.00314v3 fatcat:jt4ihvprijevnm5iwnoo34f5pa

Recommending API Function Calls and Code Snippets to Support Software Development [article]

Phuong T.Nguyen, Juri Di Rocco, Claudio Di Sipio, Davide Di Ruscio, Massimiliano Di Penta
2021 arXiv   pre-print
To this aim, it is necessary to explore approaches to mine data from software repositories and leverage it to produce helpful recommendations.  ...  The system works on the basis of a context-aware collaborative filtering technique to extract API usages from OSS projects.  ...  CONCLUSIONS We presented FOCUS, a recommender system to provide developers with suitable API function calls and code snippets while they are programming.  ... 
arXiv:2102.07508v1 fatcat:nv7kbxtktje2pjsozjvxfbfgq4

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Giang Nguyen, Stefan Dlugolinsky, Martin Bobák, Viet Tran, Álvaro López García, Ignacio Heredia, Peter Malík, Ladislav Hluchý
2019 Artificial Intelligence Review  
An engineering team at Uber has built Pyro, a universal probabilistic programming language that uses PyTorch as backend.  ...  It is a fact that the code of many open-source tools is located on GitHub in the form of repositories (GitHub 2018).  ... 
doi:10.1007/s10462-018-09679-z fatcat:ueffoypwlva4ndo35g5gzfrpcy

Opportunities and Challenges in Code Search Tools [article]

Chao Liu and Xin Xia and David Lo and Cuiyun Gao and Xiaohu Yang and John Grundy
2020 arXiv   pre-print
[103, 111, 124] employed graph-based code mining algorithms to mine paths that start with the input example and end with the output example.  ...  For example, free-form text written in natural language is the most common query, which is widely used for general search engines [72, 115, 142] , such as GitHub search.  ... 
arXiv:2011.02297v1 fatcat:pklxhrm4g5buror5abukwlb5h4

A Systematic Literature Review of Automated Query Reformulations in Source Code Search [article]

Mohammad Masudur Rahman, Chanchal K. Roy
2021 arXiv   pre-print
They mine the logical mapping between each query and API classes from corresponding URL, construct a conditional probabilistic model, and then suggest relevant API classes to reformulate a given query.  ...  method signatures using sentence parse tree extraction, uni-gram, and probabilistic context free grammar (PCFG) S47 Kimmig et al  ... 
arXiv:2108.09646v1 fatcat:7eweftlz4bff5gitbru7et5644

API code recommendation using statistical learning from fine-grained changes

Anh Tuan Nguyen, Michael Hilton, Mihai Codoban, Hoan Anh Nguyen, Lily Mast, Eli Rademacher, Tien N. Nguyen, Danny Dig
2016 Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2016  
Learning and remembering how to use APIs is difficult. While codecompletion tools can recommend API methods, browsing a long list of API method names and their documentation is tedious.  ...  We present a novel API recommendation approach that taps into the predictive power of repetitive code changes to provide more relevant API recommendations for developers.  ...  Maddison and Tarlow [24] use probabilistic context free grammars and neuroprobabilistic language models for source code.  ... 
doi:10.1145/2950290.2950333 dblp:conf/sigsoft/NguyenHCNMRND16 fatcat:hoy5p4bdefdarnwec3blgbwebu
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