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  1. Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering (ESEC/FSE '09)
  2. Automatic steering of behavioral model inference
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Asserting and checking determinism for multithreaded programs
Darwin: an approach for debugging evolving programs
The massification and webification of systems' modeling and simulation with virtual worlds
Backward-compatible constant-time exception-protected memory
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
Qos-driven runtime adaptation of service oriented architectures
Refactoring for reentrancy
Api hyperlinking via structural overlap
Insights from expert software design practice
Fitting the pieces together: a machine-checked model of safe composition
Debugging debugging: acm sigsoft impact paper award keynote
Facilitating software refactoring with appropriate resolution order of bad smells
Verification and performance evaluation of aadl models
Software architecture: many faces, many places, yet a central discipline
Synthesizing partial component-level behavior models from system specifications
Probabilistic environments in the quantitative analysis of (non-probabilistic) behaviour models
Engineering search computing applications: vision and challenges
DebugAdvisor: a recommender system for debugging
The challenge of pervasive software to the conventional wisdom of software engineering
Static data race detection for concurrent programs with asynchronous calls
Capturing propagation of infected program states
Automated security testing of web widget interactions
On the relationship between process maturity and geographic distribution: an empirical analysis of their impact on software quality
Automatic synthesis of behavior protocols for composable web-services
Monitoring probabilistic properties
Learning from examples to improve code completion systems
Ensuring interoperable service-oriented systems through engineered self-healing
Software change dynamics: evidence from 35 java projects
Reo2MC: a tool chain for performance analysis of coordination models
Supporting automatic model inconsistency fixing
Automatic steering of behavioral model inference
Graph-based mining of multiple object usage patterns
IQ routes and HD traffic: technology insights about tomtom's time-dynamic navigation concept
Symbolic pruning of concurrent program executions
Saturation-based testing of concurrent programs
Improving bug triage with bug tossing graphs
Data flow testing of service choreography
MSeqGen: object-oriented unit-test generation via mining source code
Improving slice accuracy by compression of data and control flow paths
Practical framework constraints
Smart views for analyzing problem reports: tool demo
Behavioral automata composition for automatic topology independent verification of parameterized systems
Sireum/Topi LDP: a lightweight semi-decision procedure for optimizing symbolic execution-based analyses
Fair and balanced?: bias in bug-fix datasets
Whitening SOA testing
Evaluating recovery aware components for grid reliability
SCA: a semantic conflict analyzer for parallel changes
Towards accurate probabilistic models using state refinement
Test case comparison and clustering using program profiles and static execution
ReCrashJ: a tool for capturing and reproducing program crashes in deployed applications
Javalanche: efficient mutation testing for Java
ConcernMorph: metrics-based detection of crosscutting patterns
Srijan: a graphical toolkit for sensor network macroprogramming

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Automatic steering of behavioral model inference

Content Provider ACM Digital Library
Author Pezzè, Mauro Lo, David Mariani, Leonardo
Abstract Many testing and analysis techniques use finite state models to validate and verify the quality of software systems. Since the specification of such models is complex and time-consuming, researchers defined several techniques to extract finite state models from code and traces. Automatically generating models requires much less effort than designing them, and thus eases the verification and validation of large software systems. However, when models are inferred automatically, the precision of the mining process is critical. Behavioral models mined with imprecise processes can include many spurious behaviors, and can thus compromise the results of testing and analysis techniques that use those models. In this paper, we increase the precision of automata inferred from execution traces, by leveraging two learning techniques. We first mine execution traces to infer statistically significant temporal properties that capture relations between non consecutive and possibly distant events. We then incrementally refine a simple initial automaton by merging likely equivalent states. We identify equivalent states by analyzing set of consecutive events, and we use the inferred temporal properties to evaluate whether two equivalent states can be merged or not. We merge equivalent states only if the merging does violate any temporal property, since a merging that violates temporal properties is likely to introduce an imprecise generalization. Our generalization process that preserves temporal properties while merging states avoids breaking non-local relations, and thus solves one of the major cause of overgeneralized models. Thus, mined properties steer the learning of behavioral models. The technique is completely automated and generates an automaton that both accepts the input traces and satisfies the mined temporal properties. We evaluated our solution by comparing models inferred with and without checking mined temporal properties. Results show that our steering process can significantly improve precision without noticeable loss of recall.
Starting Page 345
Ending Page 354
Page Count 10
File Format PDF
ISBN 9781605580012
DOI 10.1145/1595696.1595761
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-08-24
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Dynamic analysis Mining automata Temporal properties
Content Type Text
Resource Type Article
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