Efficient mining of iterative patterns for software specification discovery
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Summary
This paper presents CLIPER (CLosed Iterative Pattern minER) to efficiently mine a closed set of iterative patterns, and shows the efficiency of the mining algorithm and effectiveness of the pruning strategy.
- Type
- article
- Published
- 2007-08-12
- Cited by
- 138
- References
- 37
- Access
- Open access
- OpenAlex
- https://openalex.org/W2136230992
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:9397261
Keywords
TRACE (psycholinguistics), Computer science, Program comprehension, Pruning, Data mining
References
- Core Security Patterns: Best Practices and Strategies for J2EE, Web Services, and Identity Management
- Program evolution: processes of software change
- LSCs: Breathing Life into Message Sequence Charts
- Testing Object-Oriented Systems: Models, Patterns, and Tools
- Leveraging legacy system dollars for e-business
- FreeSpan: frequent pattern-projected sequential pattern mining
- An Essay on Software Reuse
- From run-time behavior to usage scenarios: an interaction-pattern mining approach
- Mining periodic patterns with gap requirement from sequences
- CloseGraph: mining closed frequent graph patterns
- BIDE: efficient mining of frequent closed sequences
- SOBER: statistical model-based bug localization
- SMArTIC: towards building an accurate, robust and scalable specification miner
- Cecil: A Sequencing Constraint Language for Automatic Static Analysis Generation
- Perracotta: mining temporal API rules from imperfect traces
- Light-weight product-lines for evolution and maintenance of Web sites
- Experiments on the effectiveness of dataflow- and control-flow-based test adequacy criteria
- KDD-Cup 2000 organizers' report: peeling the onion
- Verifying safety policies with size properties and alias controls
- PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
Cited by
- Enhance Rule Based Detection for Software Fault Prone Modules
- Mining api specifications from source code for improving software reliability
- CISpan: Comprehensive Incremental Mining Algorithms of Closed Sequential Patterns for Multi-Versional Software Mining
- Pattern-growth based frequent serial episode discovery
- MULTIDIMENSIONAL LONGEST COMMON SUBSEQUENCE DISCOVERY From LARGE DATABASE USING DNA OPERATIONS
- Temporal mining for distributed systems
- Mining Non-overlapping Repetitive Sequential Patterns by ImprovingGSP Algorithm
- Specification Mining: A Concise Introduction
- Mining Modal Scenarios from Program Execution Traces
- Knowledge formalization and reuse in BIM-based mechanical, electrical and plumbing design coordination in new construction projects using data mining techniques
- SePaS: Word sense disambiguation by sequential patterns in sentences
- SCAN: An Approach to Label and Relate Execution Trace Segments
- Unsupervised hierarchical probabilistic segmentation of discrete events
- Dynamic object process graphs
- Data Mining for Software Engineering
- Automatic extraction of assertions from execution traces of behavioural models
- From data to knowledge mining
- Graph-based detection of library API imitations
- Mining direct antagonistic communities in signed social networks
- Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
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