Henkel et al., 2018 - Google Patents
Code vectors: Understanding programs through embedded abstracted symbolic tracesHenkel et al., 2018
View PDF- Document ID
- 92850912019268829
- Author
- Henkel J
- Lahiri S
- Liblit B
- Reps T
- Publication year
- Publication venue
- Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
External Links
Snippet
With the rise of machine learning, there is a great deal of interest in treating programs as data to be fed to learning algorithms. However, programs do not start off in a form that is immediately amenable to most off-the-shelf learning techniques. Instead, it is necessary to …
- 238000000034 method 0 abstract description 48
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
- G06F17/30684—Query execution using natural language analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2705—Parsing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2765—Recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/40—Transformations of program code
- G06F8/41—Compilation
- G06F8/43—Checking; Contextual analysis
- G06F8/436—Semantic checking
- G06F8/437—Type checking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/21—Text processing
- G06F17/22—Manipulating or registering by use of codes, e.g. in sequence of text characters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3604—Software analysis for verifying properties of programs
- G06F11/3608—Software analysis for verifying properties of programs using formal methods, e.g. model checking, abstract interpretation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/55—Detecting local intrusion or implementing counter-measures
- G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
- G06F21/562—Static detection
- G06F21/563—Static detection by source code analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Henkel et al. | Code vectors: Understanding programs through embedded abstracted symbolic traces | |
Zhang et al. | A survey of learning-based automated program repair | |
Hoang et al. | Cc2vec: Distributed representations of code changes | |
Tufano et al. | Deep learning similarities from different representations of source code | |
Alon et al. | code2vec: Learning distributed representations of code | |
Rahman et al. | Natural software revisited | |
Bavishi et al. | Context2Name: A deep learning-based approach to infer natural variable names from usage contexts | |
Alur et al. | Adding nesting structure to words | |
Gauthier et al. | Premise selection and external provers for HOL4 | |
Sakkas et al. | Type error feedback via analytic program repair | |
Wang et al. | Cocosum: Contextual code summarization with multi-relational graph neural network | |
Liu et al. | Just-in-time obsolete comment detection and update | |
Liu et al. | Modeling programs hierarchically with stack-augmented LSTM | |
Nayak et al. | Knowledge graph based automated generation of test cases in software engineering | |
Samoaa et al. | A systematic mapping study of source code representation for deep learning in software engineering | |
Sharma et al. | An exploratory study on code attention in BERT | |
Mastropaolo et al. | An empirical study on code comment completion | |
Nguyen et al. | Complementing global and local contexts in representing API descriptions to improve API retrieval tasks | |
Abzianidze | Natural solution to FraCaS entailment problems | |
Zhang et al. | Question answering in knowledge bases: A verification assisted model with iterative training | |
Qiu et al. | Deep just-in-time defect localization | |
Merrill et al. | Evaluating $ n $-Gram Novelty of Language Models Using Rusty-DAWG | |
Chen et al. | “More Than Deep Learning”: post-processing for API sequence recommendation | |
Santos et al. | Finding and correcting syntax errors using recurrent neural networks | |
Jiang et al. | Heuristic and neural network based prediction of project-specific api member access |