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API learning:

  • A field study of API learning obstacles
  • Deep API Learning
  • An unsupervised approach for discovering relevant tutorial fragments for APIs
  • Asking and answering questions about unfamiliar APIs: An exploratory study
  • Automated Construction Of a Software-specific Word Similarity Database
  • Discovering Information explaining API types using text classification
  • Exploring API embedding for API usages and applications
  • Graph-based mining of multiple object usage patterns
  • Export:Detecting and visualizing API usages in large source code repositories
  • Exposing behavioral differences in cross-language API mapping relations
  • How can I use this method?
  • MAPO: Mining API usages from open source repositories
  • Mining succinct and high-coverage APIusage patterns from source code
  • Miningmulti-level API usage patterns
  • Mining Preconditions of APIs in large-scale code corpus
  • PR-Miner: Automatically extracting implicit programming rules and detecting violations in large software code
  • Parameter-free probabilistic API mining at github scale
  • Portfolio: Finding Relevant Functions and their Usage
  • Recovering TraceabilityLinks between an API and its Learning Resources
  • Sourcerer: mining and searching internet-scale software repositories
  • The end-to-end use of source code examples: An exploratory study
  • data-oriented usage patterns in JavaScript web applications
  • “Jumping ThroughHoops”: Why do Java developers struggle with cryptography APIs?
  • What makes APIs hard to learn?answers from developers
  • Automated API Property Inference Techniques
  • Automatic discovery of function mappings between similar libraries,'
  • Code How:Effective code search based on API understanding and extended boolean model (e)
  • Could WeInfer Unordered API Usage Patterns Only Using the Library SourceCode?
  • Leveraging Crowd Knowledge For Software Comprehension and Development
  • MiningSuccinct and High-coverage API Usage Patterns from Source Code
  • PAM: Probabilistic API Miner
  • Parameter-free Probabilistic API Mining Across GitHub
  • Portfolio: finding relevant functions and their usage
  • T2API: synthesizing API code usage templates from english texts with statistical translation
  • StatisticalLearning Approach for Mining API Usage Mappings for Code Migration
  • Taxonomies in software engineering: A systematic mapping study and a revised taxonomy development method
  • Synthesizing API Usage Examples
  • Using Structural Context to RecommendSource Code Examples

Code reuse

  • Hunter: next-generation code reuse for java

API misuse

  • A systematic evaluation of static API-misuse detectors
  • API change and fault proneness: a threat to the success of Android apps
  • DetectingObject Usage Anomalies

API docs

  • An empirical study on evolution of API documentation
  • Analyzing APIs documentation and code to detect directive defects
  • Augmenting api documentation with insights from stack overflow
  • Improving API documentation usability with knowledge pushing
  • Inferring resource specifications from natural language API documentation
  • What Should Developers Be Aware of? An empirical study on the directives of APIdocumentation
  • Analyzing andMining a Code Search Engine Usage Log
  • Automated API property inference techniques
  • Automatically learning semantic features for defect prediction
  • How API Documentation Fails
  • Codes: Mining Source Code Descriptions from DevelopersDiscussions
  • How do API documentation and static typing affect API usability?
  • Identifying word relations in software: A comparative study of semantic similarity tools
  • Documenting apis with crowd knowledge: a coverage analysis based on question types

Code generation

  • Latent predictor networks for code generation
  • One End-to-end program generation from user intention by deep neural networks
  • Statistical translation of English texts to API code templates
  • Structured generative models of natural source code
  • Nlp2code: Code snippet content assist via natural language tasks

#Comment generation

  • AutoComment: MiningQuestion and Answer sites for Automatic CommentGeneration
  • A Convolutional attention network for extreme summarization of source code
  • Bimodal Modelling of source code and natural language
  • AnswerBot: automated generation of answer summary to developersź technical questions
  • Deep code comment generator
  • Clocom: Mining existing source code for automatic comment generation
  • Comment generation for source code:State of the art, challenges and opportunities
  • Mining API Patterns As PartialOrders from Source Code: From Usage Scenarios to Specifications
  • Recommending source code locations for system specific transformations

#API recommendation

  • Automated LibraryRecommendation
  • Automatic Recommendation Of API Methods from Feature Requests
  • Bing developer assistant: improving developer productivity by recommending sample code
  • CodeHow: effective code search based onAPI understanding and extended boolean model
  • Example-centric programming: integrating web search into the development environment
  • Inferring method specifications from natural language APIdescriptions
  • Deep Code Search
  • Mica: A web-search tool for\x0cnding API components and examples
  • Rack:Automatic api recommendation using crowdsourced knowledge
  • SWIM: SynthesizingWhat I Mean-Code Search and Idiomatic Snippet Synthesis
  • Searching ConnectedAPI Subgraph via Text Phrases
  • Towards an intelligent code search engine
  • A Spontaneous Code RecommendationTool Based on Associative Search
  • API Usage Pattern Recommendation For Software Development
  • Searching API usage example in code repositories with sorcerer API search
  • API method recommendation without worrying about the task-API knowledge gap
  • Example Overflow: Using Social media for code recommendation
  • MAPO: Mining andRecommending API Usage Patterns
  • SWIM: Synthesizing what I Mean-code search and idiomatic snippet synthesis
  • Effective reformulation of query for code search using crowdsourced knowledge and extra-large data analytics
  • More accurate recommendations for method-level changes
  • Recommending insightful comments for source code using crowdsourced knowledge
  • Searching crowd knowledge to recommend solutions for api usage tasks
  • Supporting Software Developers with a Holistic Recommender System

Code completion

  • Code Completion with statistical language models

Stackoverflow

  • Fixing recurring crash bugs via analyzing q&a sites (T)
  • How Reliable Is the Crowdsourced Knowledge of Security Implementation?
  • How developers search for code: a case study
  • How do API changes trigger Stack Overflow Discussions? a study on the Android SDK
  • How do programmers ask and answer questions on the web?: Nier track
  • Mining StackOverflow to turn the IDE into a self-confident programming prompter
  • Parseweb: A ProgrammerAssistant for Reusing Open Source Code on the Web
  • Toward deep learning software repositories
  • What are developers talking about? an analysis of topics and trends in stack overflow
  • What do developers search for on the web? EmpiricalSoftware Engineering 22
  • Duplicate question detection in stack overflow: A reproducibility study
  • Mining duplicate questions in stack overflow
  • MiningStackOverflow to Turn the IDE into a Self-confident ProgrammingPrompter
  • Nuggets miner:Assisting developers by harnessing the Stack Overflow crowd knowledge and the github traceability
  • Ranking crowd knowledge to assist software development
  • What Makes APIs Hard to Learn? Answers fromDevelopers
  • What Makes a GoodCode Example?: A Study of Programming Q&A in StackOverflow

Bug detection

  • Leveraging test generation and specification mining for automated bug detection without false positives
  • Lightweight Defect localization for Java
  • Locus: Locating bugs from software changes
  • Tracking down software bugs using automatic anomaly detection
  • Combining deep learning with information retrieval to localize buggy files for bug reports (n)
  • Improving bug localization using structured information retrieval
  • On the use of stemming for concern location and bug localization in java

Code mining

  • SpotWeb: Detecting Framework Hotspots And Coldspots via Mining Open Source Code on the Web