Li et al., 2019 - Google Patents
Improving bug detection via context-based code representation learning and attention-based neural networksLi et al., 2019
View PDF- Document ID
- 1646644128308006897
- Author
- Li Y
- Wang S
- Nguyen T
- Van Nguyen S
- Publication year
- Publication venue
- Proceedings of the ACM on Programming Languages
External Links
Snippet
Bug detection has been shown to be an effective way to help developers in detecting bugs early, thus, saving much effort and time in software development process. Recently, deep learning-based bug detection approaches have gained successes over the traditional …
- 238000001514 detection method 0 title abstract description 93
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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30477—Query execution
- G06F17/30507—Applying rules; deductive queries
-
- 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
- G06F17/30289—Database design, administration or maintenance
- G06F17/30303—Improving data quality; Data cleansing
-
- 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
- G06F11/3672—Test management
- G06F11/3676—Test management for coverage analysis
-
- 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
- G06F11/3612—Software analysis for verifying properties of programs by runtime analysis
-
- 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
- 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
- G06N5/025—Extracting rules from data
-
- 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
- G06F8/00—Arrangements for software engineering
- G06F8/70—Software maintenance or management
- G06F8/75—Structural analysis for program understanding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | Improving bug detection via context-based code representation learning and attention-based neural networks | |
Silva et al. | Refdiff 2.0: A multi-language refactoring detection tool | |
Li et al. | Vuldeepecker: A deep learning-based system for vulnerability detection | |
Malik et al. | NL2Type: Inferring JavaScript function types from natural language information | |
Silva et al. | RefDiff: Detecting refactorings in version histories | |
US20190138731A1 (en) | Method for determining defects and vulnerabilities in software code | |
Kim et al. | Dealing with noise in defect prediction | |
Sager et al. | Detecting similar Java classes using tree algorithms | |
Kapdan et al. | On the structural code clone detection problem: a survey and software metric based approach | |
Li et al. | A mining approach to obtain the software vulnerability characteristics | |
Rabin et al. | Towards demystifying dimensions of source code embeddings | |
Cao et al. | Rule-based specification mining leveraging learning to rank | |
CN113779590B (en) | A source code vulnerability detection method based on multi-dimensional representation | |
Li et al. | A Large-scale Study on API Misuses in the Wild | |
Li et al. | Guiding log revisions by learning from software evolution history | |
Yin et al. | Local and global feature based explainable feature envy detection | |
Li et al. | Logtracker: Learning log revision behaviors proactively from software evolution history | |
Bryksin et al. | Using large-scale anomaly detection on code to improve kotlin compiler | |
Meng et al. | Classifying code commits with convolutional neural networks | |
Ye et al. | Misim: A neural code semantics similarity system using the context-aware semantics structure | |
Zhang et al. | Cceyes: An effective tool for code clone detection on large-scale open source repositories | |
Juliet Thessalonica et al. | Intelligent mining of association rules based on nanopatterns for code smells detection | |
Biringa et al. | Automated user experience testing through multi-dimensional performance impact analysis | |
Shiblu | Jsdiffer: Refactoring detection in javascript | |
Tehrani et al. | DeepRace: finding data race bugs via deep learning |